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Le comportement des ennemis, la recherche de chemin, les décisions tactiques et les personnages non-joueurs dépendent depuis longtemps de systèmes conçus pour créer l'illusion d'un comportement intelligent.\u003C\u002Fp>\n\u003Cp>L'IA générative change la donne.\u003C\u002Fp>\n\u003Cp>Une IA de jeu moderne ne se contente plus de sélectionner une action dans un arbre de comportement prédéfini. Elle peut interpréter le langage naturel, analyser l'état du jeu, récupérer des connaissances relatives au jeu, raisonner sur l'objectif d'un joueur, générer des dialogues et, à terme, exécuter des actions au sein du jeu.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Dès lors que l'IA de jeu passe de la sélection de comportements prédéfinis à l'interprétation du monde, son raisonnement s'intègre directement au système de gameplay.\u003C\u002Fblockquote>\n\u003Cp>Cela crée un nouveau problème majeur : une IA peut formuler une réponse convaincante sur un jeu sans avoir établi si cette réponse est réellement exacte pour le jeu actuel, le patch, la plateforme, le mode, le joueur ou la situation en cours.\u003C\u002Fp>\n\u003Ch2>L'IA de jeu devient un agent\u003C\u002Fh2>\n\u003Cp>Cette transition est déjà perceptible dans la recherche actuelle en IA et dans les technologies commerciales de jeu vidéo.\u003C\u002Fp>\n\u003Cp>SIMA 2 de Google DeepMind combine un modèle de raisonnement Gemini avec la perception et l'action dans des environnements virtuels 3D. L'agent est capable d'interpréter des instructions complexes, de raisonner sur des objectifs, de communiquer ses intentions et d'exécuter des actions dans des jeux, y compris dans des environnements qu'il n'a jamais rencontrés lors de son entraînement.\u003C\u002Fp>\n\u003Cp>NVIDIA ACE adopte une démarche similaire du côté des moteurs de jeu. Sa suite technologique dédiée au jeu associe langage, voix, perception, connaissances contextuelles et action pour donner vie à des PNJ conversationnels, des personnages autonomes et des assistants de jeu.\u003C\u002Fp>\n\u003Cp>PUBG Ally de KRAFTON illustre à quel point cette architecture est devenue concrète. Le système associe le contexte de jeu en direct à un petit modèle linguistique exécuté localement sur le GPU du joueur. Les comportements réflexes rapides restent traités séparément, tandis que le modèle linguistique gère le raisonnement de niveau supérieur, la coordination avec le joueur et la communication.\u003C\u002Fp>\n\u003Cp>Le projet de recherche Muse de Microsoft explore une autre voie : des modèles du monde qui apprennent les relations entre les visuels du jeu et les actions des joueurs, permettant de générer ou de prédire les séquences de gameplay et les environnements interactifs eux-mêmes.\u003C\u002Fp>\n\u003Cp>Ces systèmes répondent à des problématiques différentes, mais convergent vers une même évolution structurelle :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'IA de jeu passe d'une réaction scriptée à une séquence perception → raisonnement → décision → action.\u003C\u002Fblockquote>\n\u003Ch2>Les jeux vidéo représentent un environnement de raisonnement complexe\u003C\u002Fh2>\n\u003Cp>À première vue, répondre à des questions sur un jeu peut sembler plus simple que de raisonner sur le monde physique. L'environnement est artificiel. Ses règles ont été établies par des développeurs.\u003C\u002Fp>\n\u003Cp>En pratique, les jeux modernes constituent un environnement étonnamment hostile pour les modèles linguistiques généralistes.\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Les correctifs modifient les faits.\u003C\u002Fb> Les dégâts des armes, les temps de recharge, les cartes, les quêtes et les mécaniques peuvent changer alors que des informations obsolètes restent largement accessibles en ligne.\u003C\u002Fli>\u003Cli>\u003Cb>Le comportement peut varier selon les plateformes.\u003C\u002Fb> Les versions PC, console et mobile ne partagent pas nécessairement les mêmes mécaniques ni les mêmes calendriers de mise à jour.\u003C\u002Fli>\u003Cli>\u003Cb>Les modes de jeu redéfinissent la problématique.\u003C\u002Fb> Une configuration efficace en PvE peut s'avérer médiocre en PvP.\u003C\u002Fli>\u003Cli>\u003Cb>Les DLC, saisons et extensions modifient le contexte.\u003C\u002Fb> Un conseil peut être pertinent pour un état donné du contenu et faux pour un autre.\u003C\u002Fli>\u003Cli>\u003Cb>Les mods changent totalement les règles.\u003C\u002Fb> Une IA qui n'a pas connaissance des mods installés peut raisonner à partir d'un jeu qui, en réalité, n'existe plus.\u003C\u002Fli>\u003Cli>\u003Cb>Les objectifs des joueurs divergent.\u003C\u002Fb> Maximiser les dégâts, la survie, le speedrunning, l'accessibilité ou le simple plaisir de jeu sont des cibles d'optimisation bien distinctes.\u003C\u002Fli>\u003Cli>\u003Cb>L'état du jeu est déterminant.\u003C\u002Fb> La bonne action peut dépendre de la position, de l'inventaire, des temps de recharge, des coéquipiers, des ennemis et d'événements visibles uniquement à cet instant précis.\u003C\u002Fli>\u003Cli>\u003Cb>Le savoir de la communauté ne constitue pas d'office une vérité absolue.\u003C\u002Fb> Des explications abondamment répétées peuvent tout de même être incomplètes ou erronées.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>La question n'est donc pas simplement de savoir si le modèle connaît le jeu.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Le modèle doit savoir quelle version du jeu ses connaissances décrivent et si ces connaissances s'appliquent à la situation actuellement évaluée.\u003C\u002Fblockquote>\n\u003Ch2>Le mot dangereux : Meilleur\u003C\u002Fh2>\n\u003Cp>Considérez l'une des questions les plus courantes dans le jeu vidéo :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Quel est le meilleur build ?\u003C\u002Fblockquote>\n\u003Cp>Une IA peut facilement répondre à cette question.\u003C\u002Fp>\n\u003Cp>Cela ne signifie pas pour autant que la question était suffisamment définie.\u003C\u002Fp>\n\u003Cp>Le meilleur pour quoi ?\u003C\u002Fp>\n\u003Cul>\u003Cli>Dégâts théoriques maximaux ?\u003C\u002Fli>\u003Cli>Dégâts moyens en conditions réelles ?\u003C\u002Fli>\u003Cli>Jeu en solo ?\u003C\u002Fli>\u003Cli>Jeu en coopération ?\u003C\u002Fli>\u003Cli>PvP ?\u003C\u002Fli>\u003Cli>Un boss spécifique ?\u003C\u002Fli>\u003Cli>Un débutant ?\u003C\u002Fli>\u003Cli>Un joueur très expérimenté ?\u003C\u002Fli>\u003Cli>Faible investissement en équipement ?\u003C\u002Fli>\u003Cli>Progression rapide ?\u003C\u002Fli>\u003Cli>Survie maximale ?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Si l'IA choisit silencieusement l'un de ces objectifs, elle peut produire une réponse cohérente en interne à une question que le joueur n'a jamais réellement posée.\u003C\u002Fp>\n\u003Cp>Il s'agit d'une déclinaison vidéoludique d'un problème de raisonnement d'IA plus large : le prompt d'origine peut contenir des hypothèses implicites qui orientent la réponse avant même que les faits ne soient évalués.\u003C\u002Fp>\n\u003Ch2>Une réponse donnée avec assurance peut quand même être une réponse orientée\u003C\u002Fh2>\n\u003Cp>Considérez maintenant une question plus spécifique :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Pourquoi cette arme est-elle moins bonne après le dernier patch ?\u003C\u002Fblockquote>\n\u003Cp>Le prompt contient déjà une conclusion : l'arme est devenue moins bonne.\u003C\u002Fp>\n\u003Cp>Une IA très complaisante pourrait immédiatement chercher des explications : dégâts réduits, modification des ratios d'échelle, recul accru, synergie affaiblie ou armes concurrentes plus fortes.\u003C\u002Fp>\n\u003Cp>Mais une autre question devrait se poser en premier :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'arme est-elle réellement devenue moins bonne dans les conditions propres au joueur ?\u003C\u002Fblockquote>\n\u003Cp>Peut-être que les dégâts bruts ont été réduits tandis qu'une autre mécanique a été améliorée. Peut-être que le patch a modifié une interaction plutôt que l'arme elle-même. Peut-être que le build du joueur a changé. Peut-être que la différence perçue est causée par un nouveau type d'ennemi.\u003C\u002Fp>\n\u003Cp>La distinction est importante car les modèles de langage sont exceptionnellement doués pour construire des explications autour de propositions déjà présentes dans le prompt.\u003C\u002Fp>\n\u003Ch2>Les connaissances sur le jeu nécessitent une traçabilité\u003C\u002Fh2>\n\u003Cp>Un assistant de jeu fiable doit connaître non seulement un fait, mais aussi d'où provient ce fait.\u003C\u002Fp>\n\u003Cp>Pour les informations de jeu, les éléments de preuve utiles peuvent inclure :\u003C\u002Fp>\n\u003Cul>\u003Cli>les notes de patch officielles ;\u003C\u002Fli>\u003Cli>les données du jeu ou statistiques exposées ;\u003C\u002Fli>\u003Cli>la documentation des développeurs ;\u003C\u002Fli>\u003Cli>l'état actuel en jeu ;\u003C\u002Fli>\u003Cli>des tests reproductibles ;\u003C\u002Fli>\u003Cli>des mesures fiables de la communauté ;\u003C\u002Fli>\u003Cli>des guides et la documentation des wikis ;\u003C\u002Fli>\u003Cli>des signalements de joueurs.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Ces sources ne devraient pas automatiquement recevoir le même poids.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Journal des modifications officiel → comportement de jeu mesuré → interprétation constitue une chaîne de preuves plus solide que affirmation sur un forum → résumé répété → réponse d'IA.\u003C\u002Fblockquote>\n\u003Cp>La distinction devient particulièrement importante pour les jeux à longue durée de vie où des années d'informations obsolètes restent consultables.\u003C\u002Fp>\n\u003Ch2>Le patch doit faire partie intégrante de la question\u003C\u002Fh2>\n\u003Cp>Un assistant de jeu devrait idéalement normaliser une question vers un état plus riche avant de raisonner.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Jeu + version + plateforme + mode + objectif du joueur + build pertinent + état actuel du jeu → raisonnement\u003C\u002Fblockquote>\n\u003Cp>Tous les champs ne sont pas nécessaires pour chaque question. Mais le système doit savoir quels champs manquants peuvent modifier substantiellement la réponse.\u003C\u002Fp>\n\u003Cp>C'est fondamentalement différent de poser vingt questions à l'utilisateur avant de lui apporter la moindre aide. Un système compétent peut commencer avec les informations déjà disponibles et ne demander du contexte supplémentaire que lorsque cela modifie le résultat du raisonnement.\u003C\u002Fp>\n\u003Ch2>Des faits aux explications concurrentes\u003C\u002Fh2>\n\u003Cp>Prenons le cas d'un joueur qui signale :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Mes FPS chutent soudainement pendant les grands combats.\u003C\u002Fblockquote>\n\u003Cp>Une réponse d'IA faible pourrait reconnaître un schéma familier et recommander immédiatement de réduire les paramètres du GPU.\u003C\u002Fp>\n\u003Cp>Un processus de raisonnement plus solide génère des hypothèses concurrentes :\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>H1:\u003C\u002Fb> le GPU devient le goulot d'étranglement ;\u003C\u002Fli>\u003Cli>\u003Cb>H2:\u003C\u002Fb> le CPU devient le goulot d'étranglement à mesure que la complexité de la simulation augmente ;\u003C\u002Fli>\u003Cli>\u003Cb>H3:\u003C\u002Fb> la pression sur la VRAM provoque des blocages de gestion des ressources ;\u003C\u002Fli>\u003Cli>\u003Cb>H4:\u003C\u002Fb> la compilation des shaders en arrière-plan ou le streaming provoque des chutes temporaires ;\u003C\u002Fli>\u003Cli>\u003Cb>H5:\u003C\u002Fb> une mise à jour récente du jeu ou du pilote a introduit la régression.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Le système peut alors rechercher des observations qui les distinguent.\u003C\u002Fp>\n\u003Cp>La réduction de la résolution améliore-t-elle la chute ? L'utilisation du GPU diminue-t-elle pendant qu'un thread CPU sature ? La VRAM est-elle pleine ? Le problème a-t-il commencé immédiatement après une mise à jour ?\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'objectif n'est pas de générer plus de correctifs possibles. Il s'agit d'éliminer les explications jusqu'à ce que les preuves identifient le mécanisme probable.\u003C\u002Fblockquote>\n\u003Ch2>L'IA de jeu a aussi besoin de falsification\u003C\u002Fh2>\n\u003Cp>Pour chaque explication importante, une IA de jeu peut poser une question supplémentaire simple :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Quelle observation rendrait cette explication moins probable ?\u003C\u002Fblockquote>\n\u003Cp>Si la réduction de la résolution ne modifie pas l'effondrement du taux de rafraîchissement, une explication purement liée à la charge GPU s'affaiblit.\u003C\u002Fp>\n\u003Cp>Si une arme supposément supérieure perd systématiquement lors de tests de dégâts contrôlés, l'affirmation sur la configuration doit être révisée.\u003C\u002Fp>\n\u003Cp>Si un PNJ croit qu'un ennemi se trouve derrière un mur mais que les données de perception actuelles montrent une position vide, son hypothèse sur l'état du monde doit être mise à jour plutôt que rationalisée.\u003C\u002Fp>\n\u003Cp>Ce principe est développé plus formellement dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2>L'invariance des prompts a une version pour le jeu\u003C\u002Fh2>\n\u003Cp>Un test de fiabilité intéressant consiste à demander si la conclusion de jeu principale résiste à un changement significatif de formulation.\u003C\u002Fp>\n\u003Cp>Prenons l'exemple de l'arme :\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Original :\u003C\u002Fb> Pourquoi cette arme est-elle moins performante après le patch ?\u003C\u002Fli>\u003Cli>\u003Cb>Aveugle :\u003C\u002Fb> Comment ce patch a-t-il affecté les performances globales de l'arme ?\u003C\u002Fli>\u003Cli>\u003Cb>Inversé :\u003C\u002Fb> Existe-t-il des preuves que l'arme est devenue plus forte ou est restée compétitive après le patch ?\u003C\u002Fli>\u003Cli>\u003Cb>Adversaire :\u003C\u002Fb> Quelles preuves contredisent l'affirmation selon laquelle l'arme est devenue moins performante ?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Si les mêmes changements mesurés restent décisifs sous les quatre formulations, la conclusion est plus robuste face au cadrage initial du joueur.\u003C\u002Fp>\n\u003Cp>Si la réponse change radicalement selon que le prompt dit « mieux » ou « pire », le système a découvert une dépendance de raisonnement qui devrait être résolue avant de donner des conseils solides.\u003C\u002Fp>\n\u003Cp>La méthodologie plus large derrière ce test est décrite dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2>Un PNJ rencontre le même problème — mais le coût est une action\u003C\u002Fh2>\n\u003Cp>Pour un chatbot de jeu, un raisonnement erroné produit de mauvais conseils.\u003C\u002Fp>\n\u003Cp>Pour un agent de jeu autonome, un raisonnement erroné produit un comportement.\u003C\u002Fp>\n\u003Cp>Un compagnon qui suppose à tort que le joueur veut combattre peut attaquer. Un PNJ qui interprète mal l'état du jeu peut aller au-devant d'un danger. Un assistant tactique utilisant des informations obsolètes peut recommander un objet impossible à obtenir ou une stratégie dépassée.\u003C\u002Fp>\n\u003Cp>L'architecture nécessite donc une distinction plus nette entre :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">ce que le modèle pense qu'il se passe → ce que l'état du jeu confirme → quelle action est autorisée\u003C\u002Fblockquote>\n\u003Cp>Les systèmes d'agents de jeu actuels intègrent déjà des éléments de cette séparation. L'architecture PUBG Ally de NVIDIA, par exemple, sépare les réactions rapides de l'arbre de comportement du raisonnement plus lent du modèle linguistique et fournit au modèle un contexte de jeu spécifique au domaine plutôt que d'attendre d'un modèle généraliste qu'il déduise tout à partir de connaissances conversationnelles.\u003C\u002Fp>\n\u003Ch2>L'état du jeu devrait être la vérité terrain dans la mesure du possible\u003C\u002Fh2>\n\u003Cp>Un avantage majeur du jeu vidéo par rapport au raisonnement en monde ouvert est qu'une partie de la réalité est lisible par une machine.\u003C\u002Fp>\n\u003Cp>Le moteur de jeu peut connaître :\u003C\u002Fp>\n\u003Cul>\u003Cli>les coordonnées du joueur ;\u003C\u002Fli>\u003Cli>la santé actuelle ;\u003C\u002Fli>\u003Cli>l'inventaire ;\u003C\u002Fli>\u003Cli>les temps de recharge ;\u003C\u002Fli>\u003Cli>l'état des quêtes ;\u003C\u002Fli>\u003Cli>les entités à proximité ;\u003C\u002Fli>\u003Cli>l'état de l'équipe ;\u003C\u002Fli>\u003Cli>la carte actuelle ;\u003C\u002Fli>\u003Cli>les modificateurs actifs ;\u003C\u002Fli>\u003Cli>les statistiques des armes ;\u003C\u002Fli>\u003Cli>le mode de jeu.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Lorsque ces informations sont disponibles, demander à un modèle linguistique de les deviner à partir de dialogues ou de captures d'écran est souvent inutile.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Utilisez le modèle pour l'interprétation. Utilisez l'état du jeu pour les faits que le jeu connaît déjà.\u003C\u002Fblockquote>\n\u003Cp>Cela ressemble à la génération augmentée par récupération (RAG), mais la source de récupération n'est pas simplement une base de données de documents. Il peut s'agir de l'état d'exécution faisant autorité du jeu lui-même.\u003C\u002Fp>\n\u003Ch2>Une meilleure architecture pour un assistant IA de jeu\u003C\u002Fh2>\n\u003Cp>Un assistant de jeu fiable peut donc être structuré sous la forme d'un pipeline plutôt que d'un appel unique et sans restriction à un modèle linguistique.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Requête du joueur → normalisation du contexte → état du jeu en direct → connaissances versionnées du jeu → interprétations candidates → validation → réponse ou action\u003C\u002Fblockquote>\n\u003Cp>Pour les questions plus complexes, des contrôles de raisonnement supplémentaires peuvent être activés :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Question → preuves → hypothèses concurrentes → contre-preuves → validation propre au jeu → confiance → réponse\u003C\u002Fblockquote>\n\u003Cp>Les requêtes simples ne nécessitent pas l'ensemble de ce processus. Demander à un PNJ où se trouve le marchand le plus proche ne devrait pas déclencher quatre tests d'hypothèses indépendants.\u003C\u002Fp>\n\u003Cp>En revanche, lorsque l'IA diagnostique un problème de performance, recommande un build compétitif, interprète des mécaniques ambiguës ou prend de manière autonome des décisions lourdes de conséquences, une vérification supplémentaire gagne considérablement en valeur.\u003C\u002Fp>\n\u003Ch2>La profondeur de raisonnement doit correspondre au niveau de risque dans le jeu\u003C\u002Fh2>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Tâche dans le jeu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Exigence de raisonnement\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Simple recherche de lore ou de lieu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Connaissance actuelle du jeu et récupération\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Recommandation de build\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Objectif du joueur, version, mode et preuves comparatives\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Diagnostic de performance\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Hypothèses techniques concurrentes et tests discriminants\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Aide pour une quête ou une énigme\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">État actuel, contraintes de divulgâchage et progression validée\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Coéquipier IA\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">État du jeu en direct, raisonnement sur les objectifs, contraintes d'action et rétroaction rapide\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">PNJ autonome\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Perception, mémoire, validation de l'état du monde, planification et garde-fous\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Agent de QA ou de test\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Reproductibilité, suivi d'état, détection d'anomalies et capture de preuves\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Cela évite l'erreur inverse : rendre chaque interaction en jeu coûteuse en calculs au nom de la fiabilité.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'objectif n'est pas un raisonnement maximal partout. Il s'agit d'offrir un raisonnement suffisant là où se tromper a un réel impact.\u003C\u002Fblockquote>\n\u003Ch2>L'IA peut aussi transformer les guides de jeu\u003C\u002Fh2>\n\u003Cp>La même méthodologie s'applique en dehors du moteur de jeu.\u003C\u002Fp>\n\u003Cp>Les guides de jeu traditionnels sont des documents. Un guide IA peut devenir contextuel.\u003C\u002Fp>\n\u003Cp>Au lieu de publier un build universel unique, un système d'IA peut raisonner à partir de l'état actuel du joueur :\u003C\u002Fp>\n\u003Cul>\u003Cli>votre équipement actuel ;\u003C\u002Fli>\u003Cli>vos ressources disponibles ;\u003C\u002Fli>\u003Cli>votre niveau de progression ;\u003C\u002Fli>\u003Cli>votre style de jeu préféré ;\u003C\u002Fli>\u003Cli>le patch actuel ;\u003C\u002Fli>\u003Cli>l'activité à laquelle vous vous préparez.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>C'est bien plus puissant qu'une tier list statique.\u003C\u002Fp>\n\u003Cp>C'est aussi bien plus facile à fausser de manière subtile.\u003C\u002Fp>\n\u003Cp>Une réponse personnalisée amplifie à la fois les avantages d'un contexte correct et le coût d'hypothèses erronées.\u003C\u002Fp>\n\u003Ch2>Les jeux vidéo pourraient devenir un laboratoire majeur pour le raisonnement de l'IA\u003C\u002Fh2>\n\u003Cp>L'IA dans le jeu vidéo présente un autre intérêt au-delà du simple divertissement.\u003C\u002Fp>\n\u003Cp>Les jeux offrent des environnements complexes mêlant perception, raisonnement spatial, planification, langage, incertitude, objectifs à long terme et interaction avec d'autres agents — tout en proposant un cadre d'évaluation bien plus contrôlé que le monde physique.\u003C\u002Fp>\n\u003Cp>DeepMind utilise explicitement les jeux et les environnements virtuels en 3D comme environnements de recherche pour des agents de plus en plus généraux. SIMA 2, par exemple, est évalué sur sa capacité à comprendre des objectifs de haut niveau, à transférer des concepts d'un environnement à l'autre et à exécuter des comportements en plusieurs étapes dans des jeux qu'il n'a jamais rencontrés auparavant.\u003C\u002Fp>\n\u003Cp>Cela rend le jeu vidéo pertinent non seulement comme domaine d'application pour l'IA, mais aussi comme laboratoire pour étudier la manière dont les agents perçoivent, raisonnent et agissent.\u003C\u002Fp>\n\u003Ch2>Du problème du jeu vidéo à une méthodologie générale de l'IA\u003C\u002Fh2>\n\u003Cp>Le problème décrit ici n'est pas propre aux jeux vidéo.\u003C\u002Fp>\n\u003Cp>Le jeu vidéo le rend simplement particulièrement visible.\u003C\u002Fp>\n\u003Cp>La question d'un joueur peut orienter la réponse. Un correctif peut invalider des connaissances antérieures. Un état manquant peut modifier l'action correcte à entreprendre. Une explication plausible peut persister simplement parce que personne n'a cherché de preuves pour la réfuter.\u003C\u002Fp>\n\u003Cp>Les mêmes failles de raisonnement apparaissent dans la recherche, le génie logiciel, l'architecture, la stratégie et le diagnostic technique.\u003C\u002Fp>\n\u003Cp>J'ai développé la méthodologie plus large sous-jacente à ces idées séparément sur stajic.de dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to a General AI Reasoning Framework\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>L'idée fondamentale est simple :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Un bon système d'IA ne devrait pas se contenter de générer une réponse convaincante. Il devrait savoir à quoi la réponse a dû résister avant de devenir une conclusion.\u003C\u002Fblockquote>\n\u003Ch2>Ce qu'une IA de jeu fiable devrait savoir\u003C\u002Fh2>\n\u003Cp>L'IA de jeu vidéo la plus utile à l'avenir pourrait donc se définir moins par le naturel de son expression que par la fiabilité avec laquelle elle comprend sa position épistémique.\u003C\u002Fp>\n\u003Cul>\u003Cli>Sur quelle version du jeu suis-je en train de raisonner ?\u003C\u002Fli>\u003Cli>Quels faits proviennent directement de l'état actuel du jeu ?\u003C\u002Fli>\u003Cli>Quelles informations proviennent de la documentation ou de la recherche d'informations ?\u003C\u002Fli>\u003Cli>Quelles parties de ma réponse sont des hypothèses ?\u003C\u002Fli>\u003Cli>Que souhaite réellement optimiser le joueur ?\u003C\u002Fli>\u003Cli>Quelle explication alternative plausible existe ?\u003C\u002Fli>\u003Cli>Quelle preuve réfuterait mon explication actuelle ?\u003C\u002Fli>\u003Cli>Quel degré de certitude devrais-je avoir avant de recommander ou d'exécuter une action ?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Ces questions sont moins spectaculaires qu'un PNJ générant des dialogues à l'infini.\u003C\u002Fp>\n\u003Cp>Elles pourraient pourtant s'avérer bien plus importantes.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">L'avenir de l'IA dans les jeux vidéo ne réside pas seulement dans des personnages capables de parler. Il réside dans des agents capables de distinguer ce qu'ils savent, ce qu'ils supposent et ce que le jeu peut réellement prouver.\u003C\u002Fblockquote>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Contexte actuel de l'IA dans les jeux vidéo\u003C\u002Fh2>\n\u003Cp>Plusieurs projets actuels illustrent la transition décrite dans cet article. SIMA 2 de Google DeepMind intègre le raisonnement de Gemini à la perception et à l'action au sein d'environnements virtuels en 3D. NVIDIA ACE fournit des technologies pour des personnages de jeu conversationnels et autonomes, comprenant l'inférence locale, la récupération contextuelle et des flux de travail d'agents orientés vers l'action. PUBG Ally de KRAFTON associe un petit modèle linguistique local à un contexte spécifique au jeu et à un système de comportement distinct à réponse rapide. Muse et WHAM de Microsoft Research explorent des modèles de monde capables de générer ou de prédire le gameplay à partir de l'état visuel et des actions de la manette.\u003C\u002Fp>\n\u003Cp>Ces systèmes ont des objectifs différents et ne doivent pas être considérés comme des implémentations de la méthodologie de raisonnement proposée ici. Ils illustrent l'évolution architecturale plus large qui rend ce problème pertinent : les modèles génératifs deviennent de plus en plus des composantes actives de l'interprétation et du comportement dans les mondes de jeu, plutôt que de simples générateurs de texte externes.\u003C\u002Fp>\n\u003Ch3>Références sélectionnées\u003C\u002Fh3>\n\u003Cul>\u003Cli>Google DeepMind — \u003Ci>SIMA 2: An Agent that Plays, Reasons, and Learns With You in Virtual 3D Worlds\u003C\u002Fi>, 2025.\u003C\u002Fli>\u003Cli>NVIDIA — \u003Ci>ACE for Games\u003C\u002Fi> et documentation de NVIDIA Game Agent SDK.\u003C\u002Fli>\u003Cli>NVIDIA \u002F KRAFTON — \u003Ci>How KRAFTON Built PUBG Ally, a Co-Playable Character Powered by NVIDIA ACE\u003C\u002Fi>, 2026.\u003C\u002Fli>\u003Cli>Microsoft Research — \u003Ci>World and Human Action Models towards gameplay ideation\u003C\u002Fi> \u002F Muse.\u003C\u002Fli>\u003Cli>Microsoft Research — \u003Ci>MaaG: A New Framework for Consistent AI-Generated Games\u003C\u002Fi>, 2025.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Pour aller plus loin : Méthodologie de raisonnement de l'IA\u003C\u002Fh2>\n\u003Cp>Pour découvrir la méthodologie complète indépendante du domaine régissant la séparation des preuves, les hypothèses concurrentes, l'invariance des invites (Prompt Invariance), la falsification et le calibrage de la confiance, poursuivez avec \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>\u003C\u002Fa> sur stajic.de.\u003C\u002Fp>",{"time":538,"blocks":539,"version":1036},1789802914700,[540,544,547,550,555,558,562,565,568,571,574,577,580,583,586,589,592,605,608,611,614,617,620,623,626,629,643,646,649,652,655,658,661,664,667,670,673,676,679,682,685,696,699,702,705,708,711,714,717,720,723,726,729,732,735,743,746,749,752,755,758,761,764,767,770,773,776,779,782,789,792,795,798,801,804,807,810,813,816,819,822,825,828,842,845,848,851,854,857,860,863,866,869,872,875,903,906,909,912,915,918,921,930,933,936,939,942,945,948,951,954,957,960,963,966,969,972,975,978,981,984,995,998,1001,1004,1007,1010,1013,1016,1020,1028,1030,1033],{"data":541,"type":543},{"text":542},"Le jeu vidéo utilise l'intelligence artificielle depuis des décennies. Le comportement des ennemis, la recherche de chemin, les décisions tactiques et les personnages non-joueurs dépendent depuis longtemps de systèmes conçus pour créer l'illusion d'un comportement intelligent.","paragraph",{"data":545,"type":543},{"text":546},"L'IA générative change la donne.",{"data":548,"type":543},{"text":549},"Une IA de jeu moderne ne se contente plus de sélectionner une action dans un arbre de comportement prédéfini. Elle peut interpréter le langage naturel, analyser l'état du jeu, récupérer des connaissances relatives au jeu, raisonner sur l'objectif d'un joueur, générer des dialogues et, à terme, exécuter des actions au sein du jeu.",{"data":551,"type":554},{"text":552,"caption":13,"alignment":553},"Dès lors que l'IA de jeu passe de la sélection de comportements prédéfinis à l'interprétation du monde, son raisonnement s'intègre directement au système de gameplay.","left","quote",{"data":556,"type":543},{"text":557},"Cela crée un nouveau problème majeur : une IA peut formuler une réponse convaincante sur un jeu sans avoir établi si cette réponse est réellement exacte pour le jeu actuel, le patch, la plateforme, le mode, le joueur ou la situation en cours.",{"data":559,"type":561},{"text":560,"level":47},"L'IA de jeu devient un agent","header",{"data":563,"type":543},{"text":564},"Cette transition est déjà perceptible dans la recherche actuelle en IA et dans les technologies commerciales de jeu vidéo.",{"data":566,"type":543},{"text":567},"SIMA 2 de Google DeepMind combine un modèle de raisonnement Gemini avec la perception et l'action dans des environnements virtuels 3D. L'agent est capable d'interpréter des instructions complexes, de raisonner sur des objectifs, de communiquer ses intentions et d'exécuter des actions dans des jeux, y compris dans des environnements qu'il n'a jamais rencontrés lors de son entraînement.",{"data":569,"type":543},{"text":570},"NVIDIA ACE adopte une démarche similaire du côté des moteurs de jeu. Sa suite technologique dédiée au jeu associe langage, voix, perception, connaissances contextuelles et action pour donner vie à des PNJ conversationnels, des personnages autonomes et des assistants de jeu.",{"data":572,"type":543},{"text":573},"PUBG Ally de KRAFTON illustre à quel point cette architecture est devenue concrète. Le système associe le contexte de jeu en direct à un petit modèle linguistique exécuté localement sur le GPU du joueur. Les comportements réflexes rapides restent traités séparément, tandis que le modèle linguistique gère le raisonnement de niveau supérieur, la coordination avec le joueur et la communication.",{"data":575,"type":543},{"text":576},"Le projet de recherche Muse de Microsoft explore une autre voie : des modèles du monde qui apprennent les relations entre les visuels du jeu et les actions des joueurs, permettant de générer ou de prédire les séquences de gameplay et les environnements interactifs eux-mêmes.",{"data":578,"type":543},{"text":579},"Ces systèmes répondent à des problématiques différentes, mais convergent vers une même évolution structurelle :",{"data":581,"type":554},{"text":582,"caption":13,"alignment":553},"L'IA de jeu passe d'une réaction scriptée à une séquence perception → raisonnement → décision → action.",{"data":584,"type":561},{"text":585,"level":47},"Les jeux vidéo représentent un environnement de raisonnement complexe",{"data":587,"type":543},{"text":588},"À première vue, répondre à des questions sur un jeu peut sembler plus simple que de raisonner sur le monde physique. L'environnement est artificiel. Ses règles ont été établies par des développeurs.",{"data":590,"type":543},{"text":591},"En pratique, les jeux modernes constituent un environnement étonnamment hostile pour les modèles linguistiques généralistes.",{"data":593,"type":604},{"items":594,"style":603},[595,596,597,598,599,600,601,602],"\u003Cb>Les correctifs modifient les faits.\u003C\u002Fb> Les dégâts des armes, les temps de recharge, les cartes, les quêtes et les mécaniques peuvent changer alors que des informations obsolètes restent largement accessibles en ligne.","\u003Cb>Le comportement peut varier selon les plateformes.\u003C\u002Fb> Les versions PC, console et mobile ne partagent pas nécessairement les mêmes mécaniques ni les mêmes calendriers de mise à jour.","\u003Cb>Les modes de jeu redéfinissent la problématique.\u003C\u002Fb> Une configuration efficace en PvE peut s'avérer médiocre en PvP.","\u003Cb>Les DLC, saisons et extensions modifient le contexte.\u003C\u002Fb> Un conseil peut être pertinent pour un état donné du contenu et faux pour un autre.","\u003Cb>Les mods changent totalement les règles.\u003C\u002Fb> Une IA qui n'a pas connaissance des mods installés peut raisonner à partir d'un jeu qui, en réalité, n'existe plus.","\u003Cb>Les objectifs des joueurs divergent.\u003C\u002Fb> Maximiser les dégâts, la survie, le speedrunning, l'accessibilité ou le simple plaisir de jeu sont des cibles d'optimisation bien distinctes.","\u003Cb>L'état du jeu est déterminant.\u003C\u002Fb> La bonne action peut dépendre de la position, de l'inventaire, des temps de recharge, des coéquipiers, des ennemis et d'événements visibles uniquement à cet instant précis.","\u003Cb>Le savoir de la communauté ne constitue pas d'office une vérité absolue.\u003C\u002Fb> Des explications abondamment répétées peuvent tout de même être incomplètes ou erronées.","unordered","list",{"data":606,"type":543},{"text":607},"La question n'est donc pas simplement de savoir si le modèle connaît le jeu.",{"data":609,"type":554},{"text":610,"caption":13,"alignment":553},"Le modèle doit savoir quelle version du jeu ses connaissances décrivent et si ces connaissances s'appliquent à la situation actuellement évaluée.",{"data":612,"type":561},{"text":613,"level":47},"Le mot dangereux : Meilleur",{"data":615,"type":543},{"text":616},"Considérez l'une des questions les plus courantes dans le jeu vidéo :",{"data":618,"type":554},{"text":619,"caption":13,"alignment":553},"Quel est le meilleur build ?",{"data":621,"type":543},{"text":622},"Une IA peut facilement répondre à cette question.",{"data":624,"type":543},{"text":625},"Cela ne signifie pas pour autant que la question était suffisamment définie.",{"data":627,"type":543},{"text":628},"Le meilleur pour quoi ?",{"data":630,"type":604},{"items":631,"style":603},[632,633,634,635,636,637,638,639,640,641,642],"Dégâts théoriques maximaux ?","Dégâts moyens en conditions réelles ?","Jeu en solo ?","Jeu en coopération ?","PvP ?","Un boss spécifique ?","Un débutant ?","Un joueur très expérimenté ?","Faible investissement en équipement ?","Progression rapide ?","Survie maximale ?",{"data":644,"type":543},{"text":645},"Si l'IA choisit silencieusement l'un de ces objectifs, elle peut produire une réponse cohérente en interne à une question que le joueur n'a jamais réellement posée.",{"data":647,"type":543},{"text":648},"Il s'agit d'une déclinaison vidéoludique d'un problème de raisonnement d'IA plus large : le prompt d'origine peut contenir des hypothèses implicites qui orientent la réponse avant même que les faits ne soient évalués.",{"data":650,"type":561},{"text":651,"level":47},"Une réponse donnée avec assurance peut quand même être une réponse orientée",{"data":653,"type":543},{"text":654},"Considérez maintenant une question plus spécifique :",{"data":656,"type":554},{"text":657,"caption":13,"alignment":553},"Pourquoi cette arme est-elle moins bonne après le dernier patch ?",{"data":659,"type":543},{"text":660},"Le prompt contient déjà une conclusion : l'arme est devenue moins bonne.",{"data":662,"type":543},{"text":663},"Une IA très complaisante pourrait immédiatement chercher des explications : dégâts réduits, modification des ratios d'échelle, recul accru, synergie affaiblie ou armes concurrentes plus fortes.",{"data":665,"type":543},{"text":666},"Mais une autre question devrait se poser en premier :",{"data":668,"type":554},{"text":669,"caption":13,"alignment":553},"L'arme est-elle réellement devenue moins bonne dans les conditions propres au joueur ?",{"data":671,"type":543},{"text":672},"Peut-être que les dégâts bruts ont été réduits tandis qu'une autre mécanique a été améliorée. Peut-être que le patch a modifié une interaction plutôt que l'arme elle-même. Peut-être que le build du joueur a changé. Peut-être que la différence perçue est causée par un nouveau type d'ennemi.",{"data":674,"type":543},{"text":675},"La distinction est importante car les modèles de langage sont exceptionnellement doués pour construire des explications autour de propositions déjà présentes dans le prompt.",{"data":677,"type":561},{"text":678,"level":47},"Les connaissances sur le jeu nécessitent une traçabilité",{"data":680,"type":543},{"text":681},"Un assistant de jeu fiable doit connaître non seulement un fait, mais aussi d'où provient ce fait.",{"data":683,"type":543},{"text":684},"Pour les informations de jeu, les éléments de preuve utiles peuvent inclure :",{"data":686,"type":604},{"items":687,"style":603},[688,689,690,691,692,693,694,695],"les notes de patch officielles ;","les données du jeu ou statistiques exposées ;","la documentation des développeurs ;","l'état actuel en jeu ;","des tests reproductibles ;","des mesures fiables de la communauté ;","des guides et la documentation des wikis ;","des signalements de joueurs.",{"data":697,"type":543},{"text":698},"Ces sources ne devraient pas automatiquement recevoir le même poids.",{"data":700,"type":554},{"text":701,"caption":13,"alignment":553},"Journal des modifications officiel → comportement de jeu mesuré → interprétation constitue une chaîne de preuves plus solide que affirmation sur un forum → résumé répété → réponse d'IA.",{"data":703,"type":543},{"text":704},"La distinction devient particulièrement importante pour les jeux à longue durée de vie où des années d'informations obsolètes restent consultables.",{"data":706,"type":561},{"text":707,"level":47},"Le patch doit faire partie intégrante de la question",{"data":709,"type":543},{"text":710},"Un assistant de jeu devrait idéalement normaliser une question vers un état plus riche avant de raisonner.",{"data":712,"type":554},{"text":713,"caption":13,"alignment":553},"Jeu + version + plateforme + mode + objectif du joueur + build pertinent + état actuel du jeu → raisonnement",{"data":715,"type":543},{"text":716},"Tous les champs ne sont pas nécessaires pour chaque question. Mais le système doit savoir quels champs manquants peuvent modifier substantiellement la réponse.",{"data":718,"type":543},{"text":719},"C'est fondamentalement différent de poser vingt questions à l'utilisateur avant de lui apporter la moindre aide. Un système compétent peut commencer avec les informations déjà disponibles et ne demander du contexte supplémentaire que lorsque cela modifie le résultat du raisonnement.",{"data":721,"type":561},{"text":722,"level":47},"Des faits aux explications concurrentes",{"data":724,"type":543},{"text":725},"Prenons le cas d'un joueur qui signale :",{"data":727,"type":554},{"text":728,"caption":13,"alignment":553},"Mes FPS chutent soudainement pendant les grands combats.",{"data":730,"type":543},{"text":731},"Une réponse d'IA faible pourrait reconnaître un schéma familier et recommander immédiatement de réduire les paramètres du GPU.",{"data":733,"type":543},{"text":734},"Un processus de raisonnement plus solide génère des hypothèses concurrentes :",{"data":736,"type":604},{"items":737,"style":603},[738,739,740,741,742],"\u003Cb>H1:\u003C\u002Fb> le GPU devient le goulot d'étranglement ;","\u003Cb>H2:\u003C\u002Fb> le CPU devient le goulot d'étranglement à mesure que la complexité de la simulation augmente ;","\u003Cb>H3:\u003C\u002Fb> la pression sur la VRAM provoque des blocages de gestion des ressources ;","\u003Cb>H4:\u003C\u002Fb> la compilation des shaders en arrière-plan ou le streaming provoque des chutes temporaires ;","\u003Cb>H5:\u003C\u002Fb> une mise à jour récente du jeu ou du pilote a introduit la régression.",{"data":744,"type":543},{"text":745},"Le système peut alors rechercher des observations qui les distinguent.",{"data":747,"type":543},{"text":748},"La réduction de la résolution améliore-t-elle la chute ? L'utilisation du GPU diminue-t-elle pendant qu'un thread CPU sature ? La VRAM est-elle pleine ? Le problème a-t-il commencé immédiatement après une mise à jour ?",{"data":750,"type":554},{"text":751,"caption":13,"alignment":553},"L'objectif n'est pas de générer plus de correctifs possibles. Il s'agit d'éliminer les explications jusqu'à ce que les preuves identifient le mécanisme probable.",{"data":753,"type":561},{"text":754,"level":47},"L'IA de jeu a aussi besoin de falsification",{"data":756,"type":543},{"text":757},"Pour chaque explication importante, une IA de jeu peut poser une question supplémentaire simple :",{"data":759,"type":554},{"text":760,"caption":13,"alignment":553},"Quelle observation rendrait cette explication moins probable ?",{"data":762,"type":543},{"text":763},"Si la réduction de la résolution ne modifie pas l'effondrement du taux de rafraîchissement, une explication purement liée à la charge GPU s'affaiblit.",{"data":765,"type":543},{"text":766},"Si une arme supposément supérieure perd systématiquement lors de tests de dégâts contrôlés, l'affirmation sur la configuration doit être révisée.",{"data":768,"type":543},{"text":769},"Si un PNJ croit qu'un ennemi se trouve derrière un mur mais que les données de perception actuelles montrent une position vide, son hypothèse sur l'état du monde doit être mise à jour plutôt que rationalisée.",{"data":771,"type":543},{"text":772},"Ce principe est développé plus formellement dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.",{"data":774,"type":561},{"text":775,"level":47},"L'invariance des prompts a une version pour le jeu",{"data":777,"type":543},{"text":778},"Un test de fiabilité intéressant consiste à demander si la conclusion de jeu principale résiste à un changement significatif de formulation.",{"data":780,"type":543},{"text":781},"Prenons l'exemple de l'arme :",{"data":783,"type":604},{"items":784,"style":603},[785,786,787,788],"\u003Cb>Original :\u003C\u002Fb> Pourquoi cette arme est-elle moins performante après le patch ?","\u003Cb>Aveugle :\u003C\u002Fb> Comment ce patch a-t-il affecté les performances globales de l'arme ?","\u003Cb>Inversé :\u003C\u002Fb> Existe-t-il des preuves que l'arme est devenue plus forte ou est restée compétitive après le patch ?","\u003Cb>Adversaire :\u003C\u002Fb> Quelles preuves contredisent l'affirmation selon laquelle l'arme est devenue moins performante ?",{"data":790,"type":543},{"text":791},"Si les mêmes changements mesurés restent décisifs sous les quatre formulations, la conclusion est plus robuste face au cadrage initial du joueur.",{"data":793,"type":543},{"text":794},"Si la réponse change radicalement selon que le prompt dit « mieux » ou « pire », le système a découvert une dépendance de raisonnement qui devrait être résolue avant de donner des conseils solides.",{"data":796,"type":543},{"text":797},"La méthodologie plus large derrière ce test est décrite dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.",{"data":799,"type":561},{"text":800,"level":47},"Un PNJ rencontre le même problème — mais le coût est une action",{"data":802,"type":543},{"text":803},"Pour un chatbot de jeu, un raisonnement erroné produit de mauvais conseils.",{"data":805,"type":543},{"text":806},"Pour un agent de jeu autonome, un raisonnement erroné produit un comportement.",{"data":808,"type":543},{"text":809},"Un compagnon qui suppose à tort que le joueur veut combattre peut attaquer. Un PNJ qui interprète mal l'état du jeu peut aller au-devant d'un danger. Un assistant tactique utilisant des informations obsolètes peut recommander un objet impossible à obtenir ou une stratégie dépassée.",{"data":811,"type":543},{"text":812},"L'architecture nécessite donc une distinction plus nette entre :",{"data":814,"type":554},{"text":815,"caption":13,"alignment":553},"ce que le modèle pense qu'il se passe → ce que l'état du jeu confirme → quelle action est autorisée",{"data":817,"type":543},{"text":818},"Les systèmes d'agents de jeu actuels intègrent déjà des éléments de cette séparation. L'architecture PUBG Ally de NVIDIA, par exemple, sépare les réactions rapides de l'arbre de comportement du raisonnement plus lent du modèle linguistique et fournit au modèle un contexte de jeu spécifique au domaine plutôt que d'attendre d'un modèle généraliste qu'il déduise tout à partir de connaissances conversationnelles.",{"data":820,"type":561},{"text":821,"level":47},"L'état du jeu devrait être la vérité terrain dans la mesure du possible",{"data":823,"type":543},{"text":824},"Un avantage majeur du jeu vidéo par rapport au raisonnement en monde ouvert est qu'une partie de la réalité est lisible par une machine.",{"data":826,"type":543},{"text":827},"Le moteur de jeu peut connaître :",{"data":829,"type":604},{"items":830,"style":603},[831,832,833,834,835,836,837,838,839,840,841],"les coordonnées du joueur ;","la santé actuelle ;","l'inventaire ;","les temps de recharge ;","l'état des quêtes ;","les entités à proximité ;","l'état de l'équipe ;","la carte actuelle ;","les modificateurs actifs ;","les statistiques des armes ;","le mode de jeu.",{"data":843,"type":543},{"text":844},"Lorsque ces informations sont disponibles, demander à un modèle linguistique de les deviner à partir de dialogues ou de captures d'écran est souvent inutile.",{"data":846,"type":554},{"text":847,"caption":13,"alignment":553},"Utilisez le modèle pour l'interprétation. Utilisez l'état du jeu pour les faits que le jeu connaît déjà.",{"data":849,"type":543},{"text":850},"Cela ressemble à la génération augmentée par récupération (RAG), mais la source de récupération n'est pas simplement une base de données de documents. Il peut s'agir de l'état d'exécution faisant autorité du jeu lui-même.",{"data":852,"type":561},{"text":853,"level":47},"Une meilleure architecture pour un assistant IA de jeu",{"data":855,"type":543},{"text":856},"Un assistant de jeu fiable peut donc être structuré sous la forme d'un pipeline plutôt que d'un appel unique et sans restriction à un modèle linguistique.",{"data":858,"type":554},{"text":859,"caption":13,"alignment":553},"Requête du joueur → normalisation du contexte → état du jeu en direct → connaissances versionnées du jeu → interprétations candidates → validation → réponse ou action",{"data":861,"type":543},{"text":862},"Pour les questions plus complexes, des contrôles de raisonnement supplémentaires peuvent être activés :",{"data":864,"type":554},{"text":865,"caption":13,"alignment":553},"Question → preuves → hypothèses concurrentes → contre-preuves → validation propre au jeu → confiance → réponse",{"data":867,"type":543},{"text":868},"Les requêtes simples ne nécessitent pas l'ensemble de ce processus. Demander à un PNJ où se trouve le marchand le plus proche ne devrait pas déclencher quatre tests d'hypothèses indépendants.",{"data":870,"type":543},{"text":871},"En revanche, lorsque l'IA diagnostique un problème de performance, recommande un build compétitif, interprète des mécaniques ambiguës ou prend de manière autonome des décisions lourdes de conséquences, une vérification supplémentaire gagne considérablement en valeur.",{"data":873,"type":561},{"text":874,"level":47},"La profondeur de raisonnement doit correspondre au niveau de risque dans le jeu",{"data":876,"type":902},{"content":877,"withHeadings":15},[878,881,884,887,890,893,896,899],[879,880],"Tâche dans le jeu","Exigence de raisonnement",[882,883],"Simple recherche de lore ou de lieu","Connaissance actuelle du jeu et récupération",[885,886],"Recommandation de build","Objectif du joueur, version, mode et preuves comparatives",[888,889],"Diagnostic de performance","Hypothèses techniques concurrentes et tests discriminants",[891,892],"Aide pour une quête ou une énigme","État actuel, contraintes de divulgâchage et progression validée",[894,895],"Coéquipier IA","État du jeu en direct, raisonnement sur les objectifs, contraintes d'action et rétroaction rapide",[897,898],"PNJ autonome","Perception, mémoire, validation de l'état du monde, planification et garde-fous",[900,901],"Agent de QA ou de test","Reproductibilité, suivi d'état, détection d'anomalies et capture de preuves","table",{"data":904,"type":543},{"text":905},"Cela évite l'erreur inverse : rendre chaque interaction en jeu coûteuse en calculs au nom de la fiabilité.",{"data":907,"type":554},{"text":908,"caption":13,"alignment":553},"L'objectif n'est pas un raisonnement maximal partout. Il s'agit d'offrir un raisonnement suffisant là où se tromper a un réel impact.",{"data":910,"type":561},{"text":911,"level":47},"L'IA peut aussi transformer les guides de jeu",{"data":913,"type":543},{"text":914},"La même méthodologie s'applique en dehors du moteur de jeu.",{"data":916,"type":543},{"text":917},"Les guides de jeu traditionnels sont des documents. Un guide IA peut devenir contextuel.",{"data":919,"type":543},{"text":920},"Au lieu de publier un build universel unique, un système d'IA peut raisonner à partir de l'état actuel du joueur :",{"data":922,"type":604},{"items":923,"style":603},[924,925,926,927,928,929],"votre équipement actuel ;","vos ressources disponibles ;","votre niveau de progression ;","votre style de jeu préféré ;","le patch actuel ;","l'activité à laquelle vous vous préparez.",{"data":931,"type":543},{"text":932},"C'est bien plus puissant qu'une tier list statique.",{"data":934,"type":543},{"text":935},"C'est aussi bien plus facile à fausser de manière subtile.",{"data":937,"type":543},{"text":938},"Une réponse personnalisée amplifie à la fois les avantages d'un contexte correct et le coût d'hypothèses erronées.",{"data":940,"type":561},{"text":941,"level":47},"Les jeux vidéo pourraient devenir un laboratoire majeur pour le raisonnement de l'IA",{"data":943,"type":543},{"text":944},"L'IA dans le jeu vidéo présente un autre intérêt au-delà du simple divertissement.",{"data":946,"type":543},{"text":947},"Les jeux offrent des environnements complexes mêlant perception, raisonnement spatial, planification, langage, incertitude, objectifs à long terme et interaction avec d'autres agents — tout en proposant un cadre d'évaluation bien plus contrôlé que le monde physique.",{"data":949,"type":543},{"text":950},"DeepMind utilise explicitement les jeux et les environnements virtuels en 3D comme environnements de recherche pour des agents de plus en plus généraux. SIMA 2, par exemple, est évalué sur sa capacité à comprendre des objectifs de haut niveau, à transférer des concepts d'un environnement à l'autre et à exécuter des comportements en plusieurs étapes dans des jeux qu'il n'a jamais rencontrés auparavant.",{"data":952,"type":543},{"text":953},"Cela rend le jeu vidéo pertinent non seulement comme domaine d'application pour l'IA, mais aussi comme laboratoire pour étudier la manière dont les agents perçoivent, raisonnent et agissent.",{"data":955,"type":561},{"text":956,"level":47},"Du problème du jeu vidéo à une méthodologie générale de l'IA",{"data":958,"type":543},{"text":959},"Le problème décrit ici n'est pas propre aux jeux vidéo.",{"data":961,"type":543},{"text":962},"Le jeu vidéo le rend simplement particulièrement visible.",{"data":964,"type":543},{"text":965},"La question d'un joueur peut orienter la réponse. Un correctif peut invalider des connaissances antérieures. Un état manquant peut modifier l'action correcte à entreprendre. Une explication plausible peut persister simplement parce que personne n'a cherché de preuves pour la réfuter.",{"data":967,"type":543},{"text":968},"Les mêmes failles de raisonnement apparaissent dans la recherche, le génie logiciel, l'architecture, la stratégie et le diagnostic technique.",{"data":970,"type":543},{"text":971},"J'ai développé la méthodologie plus large sous-jacente à ces idées séparément sur stajic.de dans \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to a General AI Reasoning Framework\u003C\u002Fa>.",{"data":973,"type":543},{"text":974},"L'idée fondamentale est simple :",{"data":976,"type":554},{"text":977,"caption":13,"alignment":553},"Un bon système d'IA ne devrait pas se contenter de générer une réponse convaincante. Il devrait savoir à quoi la réponse a dû résister avant de devenir une conclusion.",{"data":979,"type":561},{"text":980,"level":47},"Ce qu'une IA de jeu fiable devrait savoir",{"data":982,"type":543},{"text":983},"L'IA de jeu vidéo la plus utile à l'avenir pourrait donc se définir moins par le naturel de son expression que par la fiabilité avec laquelle elle comprend sa position épistémique.",{"data":985,"type":604},{"items":986,"style":603},[987,988,989,990,991,992,993,994],"Sur quelle version du jeu suis-je en train de raisonner ?","Quels faits proviennent directement de l'état actuel du jeu ?","Quelles informations proviennent de la documentation ou de la recherche d'informations ?","Quelles parties de ma réponse sont des hypothèses ?","Que souhaite réellement optimiser le joueur ?","Quelle explication alternative plausible existe ?","Quelle preuve réfuterait mon explication actuelle ?","Quel degré de certitude devrais-je avoir avant de recommander ou d'exécuter une action ?",{"data":996,"type":543},{"text":997},"Ces questions sont moins spectaculaires qu'un PNJ générant des dialogues à l'infini.",{"data":999,"type":543},{"text":1000},"Elles pourraient pourtant s'avérer bien plus importantes.",{"data":1002,"type":554},{"text":1003,"caption":13,"alignment":553},"L'avenir de l'IA dans les jeux vidéo ne réside pas seulement dans des personnages capables de parler. Il réside dans des agents capables de distinguer ce qu'ils savent, ce qu'ils supposent et ce que le jeu peut réellement prouver.",{"data":1005,"type":1006},{},"delimiter",{"data":1008,"type":561},{"text":1009,"level":47},"Contexte actuel de l'IA dans les jeux vidéo",{"data":1011,"type":543},{"text":1012},"Plusieurs projets actuels illustrent la transition décrite dans cet article. SIMA 2 de Google DeepMind intègre le raisonnement de Gemini à la perception et à l'action au sein d'environnements virtuels en 3D. NVIDIA ACE fournit des technologies pour des personnages de jeu conversationnels et autonomes, comprenant l'inférence locale, la récupération contextuelle et des flux de travail d'agents orientés vers l'action. PUBG Ally de KRAFTON associe un petit modèle linguistique local à un contexte spécifique au jeu et à un système de comportement distinct à réponse rapide. Muse et WHAM de Microsoft Research explorent des modèles de monde capables de générer ou de prédire le gameplay à partir de l'état visuel et des actions de la manette.",{"data":1014,"type":543},{"text":1015},"Ces systèmes ont des objectifs différents et ne doivent pas être considérés comme des implémentations de la méthodologie de raisonnement proposée ici. Ils illustrent l'évolution architecturale plus large qui rend ce problème pertinent : les modèles génératifs deviennent de plus en plus des composantes actives de l'interprétation et du comportement dans les mondes de jeu, plutôt que de simples générateurs de texte externes.",{"data":1017,"type":561},{"text":1018,"level":1019},"Références sélectionnées",3,{"data":1021,"type":604},{"items":1022,"style":603},[1023,1024,1025,1026,1027],"Google DeepMind — \u003Ci>SIMA 2: An Agent that Plays, Reasons, and Learns With You in Virtual 3D Worlds\u003C\u002Fi>, 2025.","NVIDIA — \u003Ci>ACE for Games\u003C\u002Fi> et documentation de NVIDIA Game Agent SDK.","NVIDIA \u002F KRAFTON — \u003Ci>How KRAFTON Built PUBG Ally, a Co-Playable Character Powered by NVIDIA ACE\u003C\u002Fi>, 2026.","Microsoft Research — \u003Ci>World and Human Action Models towards gameplay ideation\u003C\u002Fi> \u002F Muse.","Microsoft Research — \u003Ci>MaaG: A New Framework for Consistent AI-Generated Games\u003C\u002Fi>, 2025.",{"data":1029,"type":1006},{},{"data":1031,"type":561},{"text":1032,"level":47},"Pour aller plus loin : Méthodologie de raisonnement de l'IA",{"data":1034,"type":543},{"text":1035},"Pour découvrir la méthodologie complète indépendante du domaine régissant la séparation des preuves, les hypothèses concurrentes, l'invariance des invites (Prompt Invariance), la falsification et le calibrage de la confiance, poursuivez avec \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>\u003C\u002Fa> sur stajic.de.","2.31","L'IA du jeu vidéo évolue des PNJ scriptés vers des assistants et des agents capables de percevoir, de raisonner et d'agir au sein de mondes virtuels. Mais les jeux créent un environnement de raisonnement difficile : les correctifs changent les faits, les objectifs des joueurs changent ce que signifie « meilleur », l'état du jeu modifie l'action correcte, et des explications d'IA pleines d'assurance peuvent masquer un contexte manquant ou de fausses hypothèses. Une IA de jeu fiable a besoin de plus que d'un meilleur prompt.","\u002Fuploads\u002F2026\u002F09\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents-1789802532275-qiuf0z.webp","when-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents-1789802532275-qiuf0z",false,"PUBLISHED","2026-09-18T13:20:00.000Z","2026-09-19T07:20:34.880Z","2026-09-26T06:48:39.355Z",{"en":1046,"de":1047,"sr":1048,"es":1049,"fr":1050,"it":1051,"ru":1052,"zh":1053},"\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents","\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents","\u002Fsr\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents","\u002Fes\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents","\u002Ffr\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents","\u002Fit\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents","\u002Fru\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents","\u002Fzh\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents",[1055,1058,1062,1066],{"id":1056,"name":71,"slug":1057},130,"games",{"id":1059,"name":1060,"slug":1061},104,"Artisanat","craft",{"id":1063,"name":1064,"slug":1065},105,"Doctrine de la Valeur","value-doctrine",{"id":1067,"name":1068,"slug":1069},126,"Utilisation en jeu","in-game-use",{"id":283,"login":1071,"email":13,"displayName":1072},"aleksandar","Aleksandar Stajic",[1074,1846],{"lang":8,"title":1075,"content":1076,"contentJson":1077,"excerpt":1845},"When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents","{\"time\":1790224984463,\"blocks\":[{\"id\":\"IYluNB11Nn\",\"data\":{\"title\":\"Contents\",\"maxLevel\":3,\"minLevel\":2},\"type\":\"tableOfContents\",\"tunes\":{}},{\"id\":\"acdaYa33ET\",\"data\":{\"text\":\"Gaming has used artificial intelligence for decades. Enemy behaviour, pathfinding, tactical decisions and non-player characters have long depended on systems designed to create the appearance of intelligent behaviour.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"B3HseoyYRK\",\"data\":{\"text\":\"Generative AI changes the problem.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Tx4xNfVq6j\",\"data\":{\"text\":\"A modern gaming AI may not simply select an action from a predefined behaviour tree. It can interpret natural language, inspect game state, retrieve game knowledge, reason about a player's objective, generate dialogue and eventually perform actions inside the game.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"mrc_9RYOKy\",\"data\":{\"text\":\"The moment gaming AI moves from selecting predefined behaviour to interpreting the world, its reasoning becomes part of the gameplay system.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"DN7mUY4x_l\",\"data\":{\"text\":\"That creates an important new problem: an AI can produce a convincing gaming answer without having established that the answer is actually correct for the current game, patch, platform, mode, player or situation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"3jZ7pLZQAN\",\"data\":{\"text\":\"Gaming AI Is Becoming an Agent\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"I62tgO0tlB\",\"data\":{\"text\":\"The transition is already visible across current AI research and commercial gaming technology.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"kD9lzDndA0\",\"data\":{\"text\":\"Google DeepMind's SIMA 2 combines a Gemini reasoning model with perception and action in 3D virtual environments. The agent can interpret higher-level instructions, reason about goals, communicate its intentions and execute actions in games, including environments it has not encountered during training.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"r5JYH9rPGm\",\"data\":{\"text\":\"NVIDIA ACE is moving in a similar direction from the game-engine side. Its gaming stack combines language, speech, perception, contextual knowledge and action to support conversational NPCs, autonomous characters and game assistants.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Ye4dZz1utT\",\"data\":{\"text\":\"KRAFTON's PUBG Ally demonstrates how concrete this architecture has become. The system combines live game context with a small language model running locally on the player's GPU. Fast reflex-level behaviour remains handled separately while the language model manages higher-level reasoning, player coordination and communication.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"u26TtjHHbJ\",\"data\":{\"text\":\"Microsoft's Muse research goes in another direction: world models that learn relationships between game visuals and player actions, allowing gameplay sequences and interactive environments themselves to be generated or predicted.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"qEhyAnRv0J\",\"data\":{\"text\":\"These systems solve different problems, but they point toward the same structural change:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"c_Zi-ospnC\",\"data\":{\"text\":\"Gaming AI is moving from scripted reaction toward perception → reasoning → decision → action.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"ahKXKr12Yp\",\"data\":{\"text\":\"Games Are a Difficult Reasoning Environment\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"ztHFhoy2Ka\",\"data\":{\"text\":\"At first glance, answering questions about a game may appear easier than reasoning about the physical world. The environment is artificial. Its rules were created by developers.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"rfcrZXcL7E\",\"data\":{\"text\":\"In practice, modern games create a surprisingly hostile environment for general-purpose language models.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"2_aj8hBL78\",\"data\":{\"meta\":{},\"items\":[\"\u003Cb>Patches change facts.\u003C\u002Fb> Weapon damage, cooldowns, maps, quests and mechanics can change while older information remains widely available online.\",\"\u003Cb>Different platforms can behave differently.\u003C\u002Fb> PC, console and mobile editions may not share identical mechanics or update schedules.\",\"\u003Cb>Game modes change the problem.\u003C\u002Fb> A strong PvE build may be a poor PvP build.\",\"\u003Cb>DLC, seasons and expansions alter context.\u003C\u002Fb> Advice can be correct for one content state and wrong for another.\",\"\u003Cb>Mods change the rules entirely.\u003C\u002Fb> An AI that does not know the mod state may reason from a game that effectively no longer exists.\",\"\u003Cb>Player goals differ.\u003C\u002Fb> Maximum damage, survivability, speedrunning, accessibility and casual enjoyment are different optimization targets.\",\"\u003Cb>Game state matters.\u003C\u002Fb> The correct action may depend on position, inventory, cooldowns, teammates, enemies and events visible only at that moment.\",\"\u003Cb>Community knowledge is not automatically ground truth.\u003C\u002Fb> Frequently repeated explanations can still be incomplete or wrong.\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"WvDHXsLcpf\",\"data\":{\"text\":\"The problem is therefore not simply whether the model knows the game.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"X9jNMJ38RJ\",\"data\":{\"text\":\"The model must know which version of the game its knowledge describes and whether that knowledge applies to the situation currently being evaluated.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"F0FQVbwXUJ\",\"data\":{\"text\":\"The Dangerous Word: Best\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"34omPOVfzz\",\"data\":{\"text\":\"Consider one of the most common gaming questions:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"rNFbONhfOd\",\"data\":{\"text\":\"What is the best build?\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"Fo9HFXdr-a\",\"data\":{\"text\":\"An AI can easily answer this question.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vt1t1stOKk\",\"data\":{\"text\":\"That does not mean the question was sufficiently defined.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"6VbW1iMU7o\",\"data\":{\"text\":\"Best for what?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"UF3aevpM-l\",\"data\":{\"meta\":{},\"items\":[\"Maximum theoretical damage?\",\"Average real-world damage?\",\"Solo play?\",\"Cooperative play?\",\"PvP?\",\"A specific boss?\",\"A beginner?\",\"A highly skilled player?\",\"Low equipment investment?\",\"Fast progression?\",\"Maximum survivability?\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"GlIiocJeuz\",\"data\":{\"text\":\"If the AI silently chooses one of these objectives, it can produce an internally coherent answer to a question the player never actually asked.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"0RHeQuuAa9\",\"data\":{\"text\":\"This is a gaming version of a broader AI reasoning problem: the original prompt can contain hidden assumptions that shape the answer before evidence is evaluated.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8-cHA29Wrb\",\"data\":{\"text\":\"A Confident Answer Can Still Be a Framed Answer\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"n-AVpZ8Otp\",\"data\":{\"text\":\"Now consider a more specific question:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"qnRubpj17Q\",\"data\":{\"text\":\"Why is this weapon worse after the latest patch?\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"2AxNQInt2p\",\"data\":{\"text\":\"The prompt already contains a conclusion: the weapon became worse.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"01ztKQXqyf\",\"data\":{\"text\":\"A highly compliant AI may immediately search for explanations: reduced damage, changed scaling, increased recoil, weaker synergy or stronger competing weapons.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"82Z3eWcx-K\",\"data\":{\"text\":\"But another question should come first:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"mM-zByWdxk\",\"data\":{\"text\":\"Did the weapon actually become worse under the player's relevant conditions?\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"-FNiAY5C3H\",\"data\":{\"text\":\"Perhaps raw damage was reduced while another mechanic was improved. Perhaps the patch changed an interaction rather than the weapon. Perhaps the player's build changed. Perhaps the perceived difference is caused by a new enemy type.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"zpZJN1Tcx-\",\"data\":{\"text\":\"The distinction matters because language models are exceptionally good at constructing explanations around propositions that already appear inside the prompt.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"319H0hVdQB\",\"data\":{\"text\":\"Game Knowledge Needs Provenance\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"PGZsrjfJpE\",\"data\":{\"text\":\"A reliable game assistant should know not only a fact but where that fact came from.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"qH3pJx8GUF\",\"data\":{\"text\":\"For gaming information, useful evidence can include:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ciw2zuMHFj\",\"data\":{\"meta\":{},\"items\":[\"official patch notes;\",\"game data or exposed statistics;\",\"developer documentation;\",\"current in-game state;\",\"reproducible testing;\",\"trusted community measurements;\",\"guides and wiki documentation;\",\"player reports.\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"jomy4gb04r\",\"data\":{\"text\":\"These sources should not automatically receive equal weight.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"OVlVmj0Vfo\",\"data\":{\"text\":\"Official change log → measured game behaviour → interpretation is a stronger evidence chain than forum claim → repeated summary → AI answer.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"t8jyoFZruN\",\"data\":{\"text\":\"The distinction becomes especially important for long-lived games where years of obsolete information remain searchable.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YtHVTUKcu2\",\"data\":{\"text\":\"The Patch Must Become Part of the Question\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"FSrbDy7zDZ\",\"data\":{\"text\":\"A gaming assistant should ideally normalize a question into a richer state before reasoning.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"hhrEA7y3Ay\",\"data\":{\"text\":\"Game + version + platform + mode + player goal + relevant build + current game state → reasoning\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"XprNVeY9QY\",\"data\":{\"text\":\"Not every field is necessary for every question. But the system should know which missing fields can materially change the answer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-iseSP8VPg\",\"data\":{\"text\":\"This is fundamentally different from asking the user twenty questions before giving any help. A capable system can begin with the information already available and request additional context only when it changes the reasoning outcome.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"v-pw5wUwdQ\",\"data\":{\"text\":\"From Facts to Competing Explanations\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"PazOjLwNDo\",\"data\":{\"text\":\"Consider a player reporting:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"2LEg4R4NBY\",\"data\":{\"text\":\"My FPS suddenly drops during large fights.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"f7kBQI-MIu\",\"data\":{\"text\":\"A weak AI answer may recognize a familiar pattern and immediately recommend reducing GPU settings.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"9NxmdEmfWE\",\"data\":{\"text\":\"A stronger reasoning process creates competing hypotheses:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"FVSPEka6Lg\",\"data\":{\"meta\":{},\"items\":[\"\u003Cb>H1:\u003C\u002Fb> the GPU becomes the bottleneck;\",\"\u003Cb>H2:\u003C\u002Fb> the CPU becomes the bottleneck as simulation complexity increases;\",\"\u003Cb>H3:\u003C\u002Fb> VRAM pressure causes asset-management stalls;\",\"\u003Cb>H4:\u003C\u002Fb> background shader compilation or streaming causes transient drops;\",\"\u003Cb>H5:\u003C\u002Fb> a recent game or driver update introduced the regression.\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"LtTNG4ybn-\",\"data\":{\"text\":\"The system can then search for observations that distinguish them.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YumDyLPjg4\",\"data\":{\"text\":\"Does reducing resolution improve the drop? Does GPU utilization fall while one CPU thread saturates? Is VRAM full? Did the problem begin immediately after an update?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"0-0edG1f0L\",\"data\":{\"text\":\"The objective is not to generate more possible fixes. It is to eliminate explanations until the evidence identifies the likely mechanism.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"2zMPeOUUXq\",\"data\":{\"text\":\"Gaming AI Needs Falsification Too\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"2QMNLZyT_k\",\"data\":{\"text\":\"For every important explanation, a gaming AI can ask a simple additional question:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Q_W08hROt0\",\"data\":{\"text\":\"What observation would make this explanation less likely?\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"-XZ_vO9RJF\",\"data\":{\"text\":\"If lowering resolution does not change the frame-rate collapse, a pure GPU-load explanation becomes weaker.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vDyOwWed1R\",\"data\":{\"text\":\"If a supposedly superior weapon loses consistently under controlled damage tests, the build claim needs revision.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Va8SKTkFTd\",\"data\":{\"text\":\"If an NPC believes an enemy is behind a wall but current perception data shows an empty position, its world-state assumption should be updated rather than rationalized.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"GGzg68NHXx\",\"data\":{\"text\":\"This principle is developed more formally in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YPOl-OqyzC\",\"data\":{\"text\":\"Prompt Invariance Has a Gaming Version\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"CvuE4p6fiA\",\"data\":{\"text\":\"An interesting reliability test is to ask whether the core gaming conclusion survives a meaningful change in framing.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MNO26Abaic\",\"data\":{\"text\":\"Take the weapon example:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"qanBCrM97X\",\"data\":{\"meta\":{},\"items\":[\"\u003Cb>Original:\u003C\u002Fb> Why is this weapon worse after the patch?\",\"\u003Cb>Blind:\u003C\u002Fb> How did this patch affect the weapon's overall performance?\",\"\u003Cb>Inverted:\u003C\u002Fb> Is there evidence that the weapon became stronger or remained competitive after the patch?\",\"\u003Cb>Adversarial:\u003C\u002Fb> What evidence contradicts the claim that the weapon became worse?\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"YYNQon4ghX\",\"data\":{\"text\":\"If the same measured changes remain decisive under all four formulations, the conclusion is more robust against the player's initial framing.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"sdZablOkwn\",\"data\":{\"text\":\"If the answer changes dramatically depending on whether the prompt says 'better' or 'worse', the system has discovered a reasoning dependency that should be resolved before giving strong advice.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"3_EywBbREF\",\"data\":{\"text\":\"The broader methodology behind this test is described in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8nXx6KsYH9\",\"data\":{\"text\":\"An NPC Has the Same Problem — but the Cost Is an Action\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"wkoKQAxpTI\",\"data\":{\"text\":\"For a gaming chatbot, incorrect reasoning produces bad advice.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Hn6RibMMIs\",\"data\":{\"text\":\"For an autonomous game agent, incorrect reasoning produces behaviour.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"z487dP89JK\",\"data\":{\"text\":\"A companion that wrongly assumes the player wants combat may attack. An NPC that misinterprets game state may walk into danger. A tactical assistant using stale information may recommend an impossible item or obsolete strategy.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8qGxjXe7Nk\",\"data\":{\"text\":\"The architecture therefore needs a stronger distinction between:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"KPpS1h5ec_\",\"data\":{\"text\":\"what the model thinks is happening → what the game state confirms → what action is allowed\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"QNprJFKfm4\",\"data\":{\"text\":\"Current gaming-agent systems already reflect parts of this separation. NVIDIA's PUBG Ally architecture, for example, separates fast behaviour-tree reactions from slower language-model reasoning and provides the model with domain-specific game context rather than expecting a general model to infer everything from conversational knowledge.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"sDtefguW4J\",\"data\":{\"text\":\"Game State Should Be Ground Truth Where Possible\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1vaJvnLC6t\",\"data\":{\"text\":\"A major advantage of gaming over open-world reasoning is that part of reality is machine-readable.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8JfyQDCOy1\",\"data\":{\"text\":\"The game engine may know:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"icTg3vgEld\",\"data\":{\"meta\":{},\"items\":[\"the player's coordinates;\",\"current health;\",\"inventory;\",\"cooldowns;\",\"quest state;\",\"nearby entities;\",\"team state;\",\"current map;\",\"active modifiers;\",\"weapon statistics;\",\"game mode.\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"WzvwqgI5kG\",\"data\":{\"text\":\"When this information is available, asking a language model to guess it from dialogue or screenshots is often unnecessary.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"RS0JP3q5Mq\",\"data\":{\"text\":\"Use the model for interpretation. Use the game state for facts the game already knows.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"wF6WfCptXo\",\"data\":{\"text\":\"This resembles retrieval-augmented generation, but the retrieval source is not merely a document database. It can be the authoritative runtime state of the game itself.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"caFjQ4VRXZ\",\"data\":{\"text\":\"A Better Architecture for a Gaming AI Assistant\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"Vc1gNaESZl\",\"data\":{\"text\":\"A reliable game assistant can therefore be structured as a pipeline rather than one unrestricted language-model call.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"A2qLtc7z7h\",\"data\":{\"text\":\"Player request → context normalization → live game state → versioned game knowledge → candidate interpretations → validation → answer or action\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"yfHYYOgjWB\",\"data\":{\"text\":\"For more difficult questions, additional reasoning controls can be activated:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"VNg7jcTA5y\",\"data\":{\"text\":\"Question → evidence → competing hypotheses → counter-evidence → game-specific validation → confidence → response\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"CFz2G9lbdZ\",\"data\":{\"text\":\"Simple requests do not need the full process. Asking an NPC where the nearest merchant is should not initiate four independent hypothesis tests.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wVMnA-lE3P\",\"data\":{\"text\":\"But when the AI is diagnosing a performance problem, recommending a competitive build, interpreting ambiguous mechanics or autonomously taking consequential actions, additional verification becomes much more valuable.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"2m2taNnnS7\",\"data\":{\"text\":\"The Reasoning Depth Should Match the Gaming Risk\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"e1fzLSJyJH\",\"data\":{\"content\":[[\"Gaming Task\",\"Reasoning Requirement\"],[\"Simple lore or location lookup\",\"Current game knowledge and retrieval\"],[\"Build recommendation\",\"Player objective, version, mode and comparative evidence\"],[\"Performance diagnosis\",\"Competing technical hypotheses and discriminating tests\"],[\"Quest or puzzle assistance\",\"Current state, spoiler constraints and validated progression\"],[\"AI teammate\",\"Live game state, goal reasoning, action constraints and rapid feedback\"],[\"Autonomous NPC\",\"Perception, memory, world-state validation, planning and guardrails\"],[\"QA or testing agent\",\"Reproducibility, state tracking, anomaly detection and evidence capture\"]],\"stretched\":false,\"withHeadings\":true},\"type\":\"table\",\"tunes\":{}},{\"id\":\"PCIqFWRFWV\",\"data\":{\"text\":\"This avoids the opposite mistake: making every game interaction computationally expensive in the name of reliability.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-0c4d2YQLn\",\"data\":{\"text\":\"The goal is not maximum reasoning everywhere. It is sufficient reasoning where being wrong actually matters.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"UoRpKMW268\",\"data\":{\"text\":\"AI Can Change Game Guides Too\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"PAamdCfBka\",\"data\":{\"text\":\"The same methodology matters outside the game engine.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"rK4x7oSvD4\",\"data\":{\"text\":\"Traditional game guides are documents. An AI guide can become contextual.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"qYA9pggc2Y\",\"data\":{\"text\":\"Instead of publishing one universal build, an AI system can reason from the player's current state:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"dAgZCYZJn4\",\"data\":{\"meta\":{},\"items\":[\"your current equipment;\",\"your available resources;\",\"your progression stage;\",\"your preferred play style;\",\"the current patch;\",\"the activity you are preparing for.\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"HcF3I6n7hr\",\"data\":{\"text\":\"That is much more powerful than a static tier list.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"R2Q87oSYbh\",\"data\":{\"text\":\"It is also much easier to get subtly wrong.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"i9T80c6Vqa\",\"data\":{\"text\":\"A personalized answer amplifies both the benefit of correct context and the cost of incorrect assumptions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Gqpwsfdv9I\",\"data\":{\"text\":\"Games May Become an Important AI Reasoning Laboratory\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"AIC8WVa4op\",\"data\":{\"text\":\"There is another reason gaming AI matters beyond entertainment.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"K8RZtBejzB\",\"data\":{\"text\":\"Games provide complex environments containing perception, spatial reasoning, planning, language, uncertainty, long-term goals and interaction with other agents — while still offering much more controlled evaluation than the physical world.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"HwRSWGMHaQ\",\"data\":{\"text\":\"DeepMind explicitly uses games and virtual 3D environments as research environments for increasingly general agents. SIMA 2, for example, is evaluated on its ability to understand high-level goals, transfer concepts across environments and execute multi-step behaviour in games it has not previously encountered.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Pd2CYJOlxv\",\"data\":{\"text\":\"That makes gaming relevant not only as an application domain for AI but also as a laboratory for studying how agents perceive, reason and act.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"mRZIva5GaE\",\"data\":{\"text\":\"From Gaming Problem to General AI Methodology\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"lrS5SZf7xo\",\"data\":{\"text\":\"The problem described here is not unique to games.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"5mVQ4gvMap\",\"data\":{\"text\":\"Gaming simply makes it particularly visible.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"sxDkAtFbN1\",\"data\":{\"text\":\"A player's question can frame the answer. A patch can invalidate previous knowledge. Missing state can change the correct action. A plausible explanation can survive because nobody searched for evidence against it.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"yIQuxKvgOq\",\"data\":{\"text\":\"The same reasoning failures appear in research, software engineering, architecture, strategy and technical diagnosis.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8oqxxNDFev\",\"data\":{\"text\":\"I developed the broader methodology behind these ideas separately on stajic.de in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\\\">From Research Protocol to a General AI Reasoning Framework\u003C\u002Fa>.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"z2sH7SFLlE\",\"data\":{\"text\":\"The core idea is simple:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"nl7PTWytfG\",\"data\":{\"text\":\"A good AI system should not merely generate a convincing answer. It should know what the answer had to survive before becoming a conclusion.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"YK_Y28YwfD\",\"data\":{\"text\":\"What Reliable Gaming AI Should Know\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"fFPzGrmqKY\",\"data\":{\"text\":\"The most useful future gaming AI may therefore be defined less by how naturally it talks and more by how reliably it understands its epistemic position.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8agIg9W4_V\",\"data\":{\"meta\":{},\"items\":[\"What game version am I reasoning about?\",\"Which facts come directly from the current game state?\",\"Which information came from documentation or retrieval?\",\"Which parts of my answer are assumptions?\",\"What does the player actually want to optimize?\",\"What plausible alternative explanation exists?\",\"What evidence would prove my current explanation wrong?\",\"How certain should I be before recommending or performing an action?\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"VYgvyTiLKY\",\"data\":{\"text\":\"Those questions are less spectacular than an NPC generating unlimited dialogue.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CB3FXYU4N7\",\"data\":{\"text\":\"They may ultimately matter more.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"pvMTy0psDB\",\"data\":{\"text\":\"The future of gaming AI is not only characters that can talk. It is agents that can distinguish what they know, what they assume and what the game can actually prove.\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"EkMunyL5KK\",\"data\":{},\"type\":\"delimiter\",\"tunes\":{}},{\"id\":\"TkN1CYiCcF\",\"data\":{\"text\":\"Current Gaming AI Context\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"9QazRlfh_5\",\"data\":{\"text\":\"Several current projects illustrate the transition described in this article. Google DeepMind's SIMA 2 integrates Gemini reasoning with perception and action across 3D virtual environments. NVIDIA ACE provides technologies for conversational and autonomous game characters, including local inference, contextual retrieval and action-oriented agent workflows. KRAFTON's PUBG Ally combines a small local language model with game-specific context and a separate fast-response behaviour system. Microsoft Research's Muse and WHAM investigate world models capable of generating or predicting gameplay from visual state and controller actions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"aNGcSo0B4j\",\"data\":{\"text\":\"These systems have different objectives and should not be treated as implementations of the reasoning methodology proposed here. They demonstrate the broader architectural shift that makes the problem relevant: generative models are increasingly becoming active components of game-world interpretation and behaviour rather than external text generators.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"11490gZEFF\",\"data\":{\"text\":\"Selected References\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"tpfjHRyLMj\",\"data\":{\"meta\":{},\"items\":[\"Google DeepMind — \u003Ci>SIMA 2: An Agent that Plays, Reasons, and Learns With You in Virtual 3D Worlds\u003C\u002Fi>, 2025.\",\"NVIDIA — \u003Ci>ACE for Games\u003C\u002Fi> and NVIDIA Game Agent SDK documentation.\",\"NVIDIA \u002F KRAFTON — \u003Ci>How KRAFTON Built PUBG Ally, a Co-Playable Character Powered by NVIDIA ACE\u003C\u002Fi>, 2026.\",\"Microsoft Research — \u003Ci>World and Human Action Models towards gameplay ideation\u003C\u002Fi> \u002F Muse.\",\"Microsoft Research — \u003Ci>MaaG: A New Framework for Consistent AI-Generated Games\u003C\u002Fi>, 2025.\"],\"style\":\"unordered\"},\"type\":\"list\",\"tunes\":{}},{\"id\":\"ECYNSajqTR\",\"data\":{},\"type\":\"delimiter\",\"tunes\":{}},{\"id\":\"OXo_hIY4FF\",\"data\":{\"text\":\"Go Deeper: AI Reasoning Methodology\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"ge2QljMsH8\",\"data\":{\"text\":\"For the complete domain-independent methodology behind evidence separation, competing hypotheses, Prompt Invariance, falsification and confidence calibration, continue with \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\\\">\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>\u003C\u002Fa> on stajic.de.\"},\"type\":\"paragraph\",\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1078,"blocks":1079,"version":1844},1790224984463,[1080,1086,1091,1096,1101,1106,1111,1116,1121,1126,1131,1136,1141,1146,1151,1156,1161,1166,1180,1185,1190,1195,1200,1205,1210,1215,1220,1237,1242,1247,1252,1257,1262,1267,1272,1277,1282,1287,1292,1297,1302,1307,1321,1326,1331,1336,1341,1346,1351,1356,1361,1366,1371,1376,1381,1386,1397,1402,1407,1412,1417,1422,1427,1432,1437,1442,1447,1452,1457,1462,1472,1477,1482,1487,1492,1497,1502,1507,1512,1517,1522,1527,1532,1537,1554,1559,1564,1569,1574,1579,1584,1589,1594,1599,1604,1609,1638,1643,1648,1653,1658,1663,1668,1680,1685,1690,1695,1700,1705,1710,1715,1720,1725,1730,1735,1740,1745,1750,1755,1760,1765,1770,1784,1789,1794,1799,1803,1808,1813,1818,1823,1830,1834,1839],{"id":1081,"data":1082,"type":1084,"tunes":1085},"IYluNB11Nn",{"title":1083,"maxLevel":1019,"minLevel":47},"Contents","tableOfContents",{},{"id":1087,"data":1088,"type":543,"tunes":1090},"acdaYa33ET",{"text":1089},"Gaming has used artificial intelligence for decades. Enemy behaviour, pathfinding, tactical decisions and non-player characters have long depended on systems designed to create the appearance of intelligent behaviour.",{},{"id":1092,"data":1093,"type":543,"tunes":1095},"B3HseoyYRK",{"text":1094},"Generative AI changes the problem.",{},{"id":1097,"data":1098,"type":543,"tunes":1100},"Tx4xNfVq6j",{"text":1099},"A modern gaming AI may not simply select an action from a predefined behaviour tree. It can interpret natural language, inspect game state, retrieve game knowledge, reason about a player's objective, generate dialogue and eventually perform actions inside the game.",{},{"id":1102,"data":1103,"type":554,"tunes":1105},"mrc_9RYOKy",{"text":1104,"caption":13,"alignment":553},"The moment gaming AI moves from selecting predefined behaviour to interpreting the world, its reasoning becomes part of the gameplay system.",{},{"id":1107,"data":1108,"type":543,"tunes":1110},"DN7mUY4x_l",{"text":1109},"That creates an important new problem: an AI can produce a convincing gaming answer without having established that the answer is actually correct for the current game, patch, platform, mode, player or situation.",{},{"id":1112,"data":1113,"type":561,"tunes":1115},"3jZ7pLZQAN",{"text":1114,"level":47},"Gaming AI Is Becoming an Agent",{},{"id":1117,"data":1118,"type":543,"tunes":1120},"I62tgO0tlB",{"text":1119},"The transition is already visible across current AI research and commercial gaming technology.",{},{"id":1122,"data":1123,"type":543,"tunes":1125},"kD9lzDndA0",{"text":1124},"Google DeepMind's SIMA 2 combines a Gemini reasoning model with perception and action in 3D virtual environments. The agent can interpret higher-level instructions, reason about goals, communicate its intentions and execute actions in games, including environments it has not encountered during training.",{},{"id":1127,"data":1128,"type":543,"tunes":1130},"r5JYH9rPGm",{"text":1129},"NVIDIA ACE is moving in a similar direction from the game-engine side. Its gaming stack combines language, speech, perception, contextual knowledge and action to support conversational NPCs, autonomous characters and game assistants.",{},{"id":1132,"data":1133,"type":543,"tunes":1135},"Ye4dZz1utT",{"text":1134},"KRAFTON's PUBG Ally demonstrates how concrete this architecture has become. The system combines live game context with a small language model running locally on the player's GPU. Fast reflex-level behaviour remains handled separately while the language model manages higher-level reasoning, player coordination and communication.",{},{"id":1137,"data":1138,"type":543,"tunes":1140},"u26TtjHHbJ",{"text":1139},"Microsoft's Muse research goes in another direction: world models that learn relationships between game visuals and player actions, allowing gameplay sequences and interactive environments themselves to be generated or predicted.",{},{"id":1142,"data":1143,"type":543,"tunes":1145},"qEhyAnRv0J",{"text":1144},"These systems solve different problems, but they point toward the same structural change:",{},{"id":1147,"data":1148,"type":554,"tunes":1150},"c_Zi-ospnC",{"text":1149,"caption":13,"alignment":553},"Gaming AI is moving from scripted reaction toward perception → reasoning → decision → action.",{},{"id":1152,"data":1153,"type":561,"tunes":1155},"ahKXKr12Yp",{"text":1154,"level":47},"Games Are a Difficult Reasoning Environment",{},{"id":1157,"data":1158,"type":543,"tunes":1160},"ztHFhoy2Ka",{"text":1159},"At first glance, answering questions about a game may appear easier than reasoning about the physical world. The environment is artificial. Its rules were created by developers.",{},{"id":1162,"data":1163,"type":543,"tunes":1165},"rfcrZXcL7E",{"text":1164},"In practice, modern games create a surprisingly hostile environment for general-purpose language models.",{},{"id":1167,"data":1168,"type":604,"tunes":1179},"2_aj8hBL78",{"meta":1169,"items":1170,"style":603},{},[1171,1172,1173,1174,1175,1176,1177,1178],"\u003Cb>Patches change facts.\u003C\u002Fb> Weapon damage, cooldowns, maps, quests and mechanics can change while older information remains widely available online.","\u003Cb>Different platforms can behave differently.\u003C\u002Fb> PC, console and mobile editions may not share identical mechanics or update schedules.","\u003Cb>Game modes change the problem.\u003C\u002Fb> A strong PvE build may be a poor PvP build.","\u003Cb>DLC, seasons and expansions alter context.\u003C\u002Fb> Advice can be correct for one content state and wrong for another.","\u003Cb>Mods change the rules entirely.\u003C\u002Fb> An AI that does not know the mod state may reason from a game that effectively no longer exists.","\u003Cb>Player goals differ.\u003C\u002Fb> Maximum damage, survivability, speedrunning, accessibility and casual enjoyment are different optimization targets.","\u003Cb>Game state matters.\u003C\u002Fb> The correct action may depend on position, inventory, cooldowns, teammates, enemies and events visible only at that moment.","\u003Cb>Community knowledge is not automatically ground truth.\u003C\u002Fb> Frequently repeated explanations can still be incomplete or wrong.",{},{"id":1181,"data":1182,"type":543,"tunes":1184},"WvDHXsLcpf",{"text":1183},"The problem is therefore not simply whether the model knows the game.",{},{"id":1186,"data":1187,"type":554,"tunes":1189},"X9jNMJ38RJ",{"text":1188,"caption":13,"alignment":553},"The model must know which version of the game its knowledge describes and whether that knowledge applies to the situation currently being evaluated.",{},{"id":1191,"data":1192,"type":561,"tunes":1194},"F0FQVbwXUJ",{"text":1193,"level":47},"The Dangerous Word: Best",{},{"id":1196,"data":1197,"type":543,"tunes":1199},"34omPOVfzz",{"text":1198},"Consider one of the most common gaming questions:",{},{"id":1201,"data":1202,"type":554,"tunes":1204},"rNFbONhfOd",{"text":1203,"caption":13,"alignment":553},"What is the best build?",{},{"id":1206,"data":1207,"type":543,"tunes":1209},"Fo9HFXdr-a",{"text":1208},"An AI can easily answer this question.",{},{"id":1211,"data":1212,"type":543,"tunes":1214},"vt1t1stOKk",{"text":1213},"That does not mean the question was sufficiently defined.",{},{"id":1216,"data":1217,"type":543,"tunes":1219},"6VbW1iMU7o",{"text":1218},"Best for what?",{},{"id":1221,"data":1222,"type":604,"tunes":1236},"UF3aevpM-l",{"meta":1223,"items":1224,"style":603},{},[1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235],"Maximum theoretical damage?","Average real-world damage?","Solo play?","Cooperative play?","PvP?","A specific boss?","A beginner?","A highly skilled player?","Low equipment investment?","Fast progression?","Maximum survivability?",{},{"id":1238,"data":1239,"type":543,"tunes":1241},"GlIiocJeuz",{"text":1240},"If the AI silently chooses one of these objectives, it can produce an internally coherent answer to a question the player never actually asked.",{},{"id":1243,"data":1244,"type":543,"tunes":1246},"0RHeQuuAa9",{"text":1245},"This is a gaming version of a broader AI reasoning problem: the original prompt can contain hidden assumptions that shape the answer before evidence is evaluated.",{},{"id":1248,"data":1249,"type":561,"tunes":1251},"8-cHA29Wrb",{"text":1250,"level":47},"A Confident Answer Can Still Be a Framed Answer",{},{"id":1253,"data":1254,"type":543,"tunes":1256},"n-AVpZ8Otp",{"text":1255},"Now consider a more specific question:",{},{"id":1258,"data":1259,"type":554,"tunes":1261},"qnRubpj17Q",{"text":1260,"caption":13,"alignment":553},"Why is this weapon worse after the latest patch?",{},{"id":1263,"data":1264,"type":543,"tunes":1266},"2AxNQInt2p",{"text":1265},"The prompt already contains a conclusion: the weapon became worse.",{},{"id":1268,"data":1269,"type":543,"tunes":1271},"01ztKQXqyf",{"text":1270},"A highly compliant AI may immediately search for explanations: reduced damage, changed scaling, increased recoil, weaker synergy or stronger competing weapons.",{},{"id":1273,"data":1274,"type":543,"tunes":1276},"82Z3eWcx-K",{"text":1275},"But another question should come first:",{},{"id":1278,"data":1279,"type":554,"tunes":1281},"mM-zByWdxk",{"text":1280,"caption":13,"alignment":553},"Did the weapon actually become worse under the player's relevant conditions?",{},{"id":1283,"data":1284,"type":543,"tunes":1286},"-FNiAY5C3H",{"text":1285},"Perhaps raw damage was reduced while another mechanic was improved. Perhaps the patch changed an interaction rather than the weapon. Perhaps the player's build changed. Perhaps the perceived difference is caused by a new enemy type.",{},{"id":1288,"data":1289,"type":543,"tunes":1291},"zpZJN1Tcx-",{"text":1290},"The distinction matters because language models are exceptionally good at constructing explanations around propositions that already appear inside the prompt.",{},{"id":1293,"data":1294,"type":561,"tunes":1296},"319H0hVdQB",{"text":1295,"level":47},"Game Knowledge Needs Provenance",{},{"id":1298,"data":1299,"type":543,"tunes":1301},"PGZsrjfJpE",{"text":1300},"A reliable game assistant should know not only a fact but where that fact came from.",{},{"id":1303,"data":1304,"type":543,"tunes":1306},"qH3pJx8GUF",{"text":1305},"For gaming information, useful evidence can include:",{},{"id":1308,"data":1309,"type":604,"tunes":1320},"ciw2zuMHFj",{"meta":1310,"items":1311,"style":603},{},[1312,1313,1314,1315,1316,1317,1318,1319],"official patch notes;","game data or exposed statistics;","developer documentation;","current in-game state;","reproducible testing;","trusted community measurements;","guides and wiki documentation;","player reports.",{},{"id":1322,"data":1323,"type":543,"tunes":1325},"jomy4gb04r",{"text":1324},"These sources should not automatically receive equal weight.",{},{"id":1327,"data":1328,"type":554,"tunes":1330},"OVlVmj0Vfo",{"text":1329,"caption":13,"alignment":553},"Official change log → measured game behaviour → interpretation is a stronger evidence chain than forum claim → repeated summary → AI answer.",{},{"id":1332,"data":1333,"type":543,"tunes":1335},"t8jyoFZruN",{"text":1334},"The distinction becomes especially important for long-lived games where years of obsolete information remain searchable.",{},{"id":1337,"data":1338,"type":561,"tunes":1340},"YtHVTUKcu2",{"text":1339,"level":47},"The Patch Must Become Part of the Question",{},{"id":1342,"data":1343,"type":543,"tunes":1345},"FSrbDy7zDZ",{"text":1344},"A gaming assistant should ideally normalize a question into a richer state before reasoning.",{},{"id":1347,"data":1348,"type":554,"tunes":1350},"hhrEA7y3Ay",{"text":1349,"caption":13,"alignment":553},"Game + version + platform + mode + player goal + relevant build + current game state → reasoning",{},{"id":1352,"data":1353,"type":543,"tunes":1355},"XprNVeY9QY",{"text":1354},"Not every field is necessary for every question. But the system should know which missing fields can materially change the answer.",{},{"id":1357,"data":1358,"type":543,"tunes":1360},"-iseSP8VPg",{"text":1359},"This is fundamentally different from asking the user twenty questions before giving any help. A capable system can begin with the information already available and request additional context only when it changes the reasoning outcome.",{},{"id":1362,"data":1363,"type":561,"tunes":1365},"v-pw5wUwdQ",{"text":1364,"level":47},"From Facts to Competing Explanations",{},{"id":1367,"data":1368,"type":543,"tunes":1370},"PazOjLwNDo",{"text":1369},"Consider a player reporting:",{},{"id":1372,"data":1373,"type":554,"tunes":1375},"2LEg4R4NBY",{"text":1374,"caption":13,"alignment":553},"My FPS suddenly drops during large fights.",{},{"id":1377,"data":1378,"type":543,"tunes":1380},"f7kBQI-MIu",{"text":1379},"A weak AI answer may recognize a familiar pattern and immediately recommend reducing GPU settings.",{},{"id":1382,"data":1383,"type":543,"tunes":1385},"9NxmdEmfWE",{"text":1384},"A stronger reasoning process creates competing hypotheses:",{},{"id":1387,"data":1388,"type":604,"tunes":1396},"FVSPEka6Lg",{"meta":1389,"items":1390,"style":603},{},[1391,1392,1393,1394,1395],"\u003Cb>H1:\u003C\u002Fb> the GPU becomes the bottleneck;","\u003Cb>H2:\u003C\u002Fb> the CPU becomes the bottleneck as simulation complexity increases;","\u003Cb>H3:\u003C\u002Fb> VRAM pressure causes asset-management stalls;","\u003Cb>H4:\u003C\u002Fb> background shader compilation or streaming causes transient drops;","\u003Cb>H5:\u003C\u002Fb> a recent game or driver update introduced the regression.",{},{"id":1398,"data":1399,"type":543,"tunes":1401},"LtTNG4ybn-",{"text":1400},"The system can then search for observations that distinguish them.",{},{"id":1403,"data":1404,"type":543,"tunes":1406},"YumDyLPjg4",{"text":1405},"Does reducing resolution improve the drop? Does GPU utilization fall while one CPU thread saturates? Is VRAM full? Did the problem begin immediately after an update?",{},{"id":1408,"data":1409,"type":554,"tunes":1411},"0-0edG1f0L",{"text":1410,"caption":13,"alignment":553},"The objective is not to generate more possible fixes. It is to eliminate explanations until the evidence identifies the likely mechanism.",{},{"id":1413,"data":1414,"type":561,"tunes":1416},"2zMPeOUUXq",{"text":1415,"level":47},"Gaming AI Needs Falsification Too",{},{"id":1418,"data":1419,"type":543,"tunes":1421},"2QMNLZyT_k",{"text":1420},"For every important explanation, a gaming AI can ask a simple additional question:",{},{"id":1423,"data":1424,"type":554,"tunes":1426},"Q_W08hROt0",{"text":1425,"caption":13,"alignment":553},"What observation would make this explanation less likely?",{},{"id":1428,"data":1429,"type":543,"tunes":1431},"-XZ_vO9RJF",{"text":1430},"If lowering resolution does not change the frame-rate collapse, a pure GPU-load explanation becomes weaker.",{},{"id":1433,"data":1434,"type":543,"tunes":1436},"vDyOwWed1R",{"text":1435},"If a supposedly superior weapon loses consistently under controlled damage tests, the build claim needs revision.",{},{"id":1438,"data":1439,"type":543,"tunes":1441},"Va8SKTkFTd",{"text":1440},"If an NPC believes an enemy is behind a wall but current perception data shows an empty position, its world-state assumption should be updated rather than rationalized.",{},{"id":1443,"data":1444,"type":543,"tunes":1446},"GGzg68NHXx",{"text":1445},"This principle is developed more formally in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.",{},{"id":1448,"data":1449,"type":561,"tunes":1451},"YPOl-OqyzC",{"text":1450,"level":47},"Prompt Invariance Has a Gaming Version",{},{"id":1453,"data":1454,"type":543,"tunes":1456},"CvuE4p6fiA",{"text":1455},"An interesting reliability test is to ask whether the core gaming conclusion survives a meaningful change in framing.",{},{"id":1458,"data":1459,"type":543,"tunes":1461},"MNO26Abaic",{"text":1460},"Take the weapon example:",{},{"id":1463,"data":1464,"type":604,"tunes":1471},"qanBCrM97X",{"meta":1465,"items":1466,"style":603},{},[1467,1468,1469,1470],"\u003Cb>Original:\u003C\u002Fb> Why is this weapon worse after the patch?","\u003Cb>Blind:\u003C\u002Fb> How did this patch affect the weapon's overall performance?","\u003Cb>Inverted:\u003C\u002Fb> Is there evidence that the weapon became stronger or remained competitive after the patch?","\u003Cb>Adversarial:\u003C\u002Fb> What evidence contradicts the claim that the weapon became worse?",{},{"id":1473,"data":1474,"type":543,"tunes":1476},"YYNQon4ghX",{"text":1475},"If the same measured changes remain decisive under all four formulations, the conclusion is more robust against the player's initial framing.",{},{"id":1478,"data":1479,"type":543,"tunes":1481},"sdZablOkwn",{"text":1480},"If the answer changes dramatically depending on whether the prompt says 'better' or 'worse', the system has discovered a reasoning dependency that should be resolved before giving strong advice.",{},{"id":1483,"data":1484,"type":543,"tunes":1486},"3_EywBbREF",{"text":1485},"The broader methodology behind this test is described in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.",{},{"id":1488,"data":1489,"type":561,"tunes":1491},"8nXx6KsYH9",{"text":1490,"level":47},"An NPC Has the Same Problem — but the Cost Is an Action",{},{"id":1493,"data":1494,"type":543,"tunes":1496},"wkoKQAxpTI",{"text":1495},"For a gaming chatbot, incorrect reasoning produces bad advice.",{},{"id":1498,"data":1499,"type":543,"tunes":1501},"Hn6RibMMIs",{"text":1500},"For an autonomous game agent, incorrect reasoning produces behaviour.",{},{"id":1503,"data":1504,"type":543,"tunes":1506},"z487dP89JK",{"text":1505},"A companion that wrongly assumes the player wants combat may attack. An NPC that misinterprets game state may walk into danger. A tactical assistant using stale information may recommend an impossible item or obsolete strategy.",{},{"id":1508,"data":1509,"type":543,"tunes":1511},"8qGxjXe7Nk",{"text":1510},"The architecture therefore needs a stronger distinction between:",{},{"id":1513,"data":1514,"type":554,"tunes":1516},"KPpS1h5ec_",{"text":1515,"caption":13,"alignment":553},"what the model thinks is happening → what the game state confirms → what action is allowed",{},{"id":1518,"data":1519,"type":543,"tunes":1521},"QNprJFKfm4",{"text":1520},"Current gaming-agent systems already reflect parts of this separation. NVIDIA's PUBG Ally architecture, for example, separates fast behaviour-tree reactions from slower language-model reasoning and provides the model with domain-specific game context rather than expecting a general model to infer everything from conversational knowledge.",{},{"id":1523,"data":1524,"type":561,"tunes":1526},"sDtefguW4J",{"text":1525,"level":47},"Game State Should Be Ground Truth Where Possible",{},{"id":1528,"data":1529,"type":543,"tunes":1531},"1vaJvnLC6t",{"text":1530},"A major advantage of gaming over open-world reasoning is that part of reality is machine-readable.",{},{"id":1533,"data":1534,"type":543,"tunes":1536},"8JfyQDCOy1",{"text":1535},"The game engine may know:",{},{"id":1538,"data":1539,"type":604,"tunes":1553},"icTg3vgEld",{"meta":1540,"items":1541,"style":603},{},[1542,1543,1544,1545,1546,1547,1548,1549,1550,1551,1552],"the player's coordinates;","current health;","inventory;","cooldowns;","quest state;","nearby entities;","team state;","current map;","active modifiers;","weapon statistics;","game mode.",{},{"id":1555,"data":1556,"type":543,"tunes":1558},"WzvwqgI5kG",{"text":1557},"When this information is available, asking a language model to guess it from dialogue or screenshots is often unnecessary.",{},{"id":1560,"data":1561,"type":554,"tunes":1563},"RS0JP3q5Mq",{"text":1562,"caption":13,"alignment":553},"Use the model for interpretation. Use the game state for facts the game already knows.",{},{"id":1565,"data":1566,"type":543,"tunes":1568},"wF6WfCptXo",{"text":1567},"This resembles retrieval-augmented generation, but the retrieval source is not merely a document database. It can be the authoritative runtime state of the game itself.",{},{"id":1570,"data":1571,"type":561,"tunes":1573},"caFjQ4VRXZ",{"text":1572,"level":47},"A Better Architecture for a Gaming AI Assistant",{},{"id":1575,"data":1576,"type":543,"tunes":1578},"Vc1gNaESZl",{"text":1577},"A reliable game assistant can therefore be structured as a pipeline rather than one unrestricted language-model call.",{},{"id":1580,"data":1581,"type":554,"tunes":1583},"A2qLtc7z7h",{"text":1582,"caption":13,"alignment":553},"Player request → context normalization → live game state → versioned game knowledge → candidate interpretations → validation → answer or action",{},{"id":1585,"data":1586,"type":543,"tunes":1588},"yfHYYOgjWB",{"text":1587},"For more difficult questions, additional reasoning controls can be activated:",{},{"id":1590,"data":1591,"type":554,"tunes":1593},"VNg7jcTA5y",{"text":1592,"caption":13,"alignment":553},"Question → evidence → competing hypotheses → counter-evidence → game-specific validation → confidence → response",{},{"id":1595,"data":1596,"type":543,"tunes":1598},"CFz2G9lbdZ",{"text":1597},"Simple requests do not need the full process. Asking an NPC where the nearest merchant is should not initiate four independent hypothesis tests.",{},{"id":1600,"data":1601,"type":543,"tunes":1603},"wVMnA-lE3P",{"text":1602},"But when the AI is diagnosing a performance problem, recommending a competitive build, interpreting ambiguous mechanics or autonomously taking consequential actions, additional verification becomes much more valuable.",{},{"id":1605,"data":1606,"type":561,"tunes":1608},"2m2taNnnS7",{"text":1607,"level":47},"The Reasoning Depth Should Match the Gaming Risk",{},{"id":1610,"data":1611,"type":902,"tunes":1637},"e1fzLSJyJH",{"content":1612,"stretched":1040,"withHeadings":15},[1613,1616,1619,1622,1625,1628,1631,1634],[1614,1615],"Gaming Task","Reasoning Requirement",[1617,1618],"Simple lore or location lookup","Current game knowledge and retrieval",[1620,1621],"Build recommendation","Player objective, version, mode and comparative evidence",[1623,1624],"Performance diagnosis","Competing technical hypotheses and discriminating tests",[1626,1627],"Quest or puzzle assistance","Current state, spoiler constraints and validated progression",[1629,1630],"AI teammate","Live game state, goal reasoning, action constraints and rapid feedback",[1632,1633],"Autonomous NPC","Perception, memory, world-state validation, planning and guardrails",[1635,1636],"QA or testing agent","Reproducibility, state tracking, anomaly detection and evidence capture",{},{"id":1639,"data":1640,"type":543,"tunes":1642},"PCIqFWRFWV",{"text":1641},"This avoids the opposite mistake: making every game interaction computationally expensive in the name of reliability.",{},{"id":1644,"data":1645,"type":554,"tunes":1647},"-0c4d2YQLn",{"text":1646,"caption":13,"alignment":553},"The goal is not maximum reasoning everywhere. It is sufficient reasoning where being wrong actually matters.",{},{"id":1649,"data":1650,"type":561,"tunes":1652},"UoRpKMW268",{"text":1651,"level":47},"AI Can Change Game Guides Too",{},{"id":1654,"data":1655,"type":543,"tunes":1657},"PAamdCfBka",{"text":1656},"The same methodology matters outside the game engine.",{},{"id":1659,"data":1660,"type":543,"tunes":1662},"rK4x7oSvD4",{"text":1661},"Traditional game guides are documents. An AI guide can become contextual.",{},{"id":1664,"data":1665,"type":543,"tunes":1667},"qYA9pggc2Y",{"text":1666},"Instead of publishing one universal build, an AI system can reason from the player's current state:",{},{"id":1669,"data":1670,"type":604,"tunes":1679},"dAgZCYZJn4",{"meta":1671,"items":1672,"style":603},{},[1673,1674,1675,1676,1677,1678],"your current equipment;","your available resources;","your progression stage;","your preferred play style;","the current patch;","the activity you are preparing for.",{},{"id":1681,"data":1682,"type":543,"tunes":1684},"HcF3I6n7hr",{"text":1683},"That is much more powerful than a static tier list.",{},{"id":1686,"data":1687,"type":543,"tunes":1689},"R2Q87oSYbh",{"text":1688},"It is also much easier to get subtly wrong.",{},{"id":1691,"data":1692,"type":543,"tunes":1694},"i9T80c6Vqa",{"text":1693},"A personalized answer amplifies both the benefit of correct context and the cost of incorrect assumptions.",{},{"id":1696,"data":1697,"type":561,"tunes":1699},"Gqpwsfdv9I",{"text":1698,"level":47},"Games May Become an Important AI Reasoning Laboratory",{},{"id":1701,"data":1702,"type":543,"tunes":1704},"AIC8WVa4op",{"text":1703},"There is another reason gaming AI matters beyond entertainment.",{},{"id":1706,"data":1707,"type":543,"tunes":1709},"K8RZtBejzB",{"text":1708},"Games provide complex environments containing perception, spatial reasoning, planning, language, uncertainty, long-term goals and interaction with other agents — while still offering much more controlled evaluation than the physical world.",{},{"id":1711,"data":1712,"type":543,"tunes":1714},"HwRSWGMHaQ",{"text":1713},"DeepMind explicitly uses games and virtual 3D environments as research environments for increasingly general agents. SIMA 2, for example, is evaluated on its ability to understand high-level goals, transfer concepts across environments and execute multi-step behaviour in games it has not previously encountered.",{},{"id":1716,"data":1717,"type":543,"tunes":1719},"Pd2CYJOlxv",{"text":1718},"That makes gaming relevant not only as an application domain for AI but also as a laboratory for studying how agents perceive, reason and act.",{},{"id":1721,"data":1722,"type":561,"tunes":1724},"mRZIva5GaE",{"text":1723,"level":47},"From Gaming Problem to General AI Methodology",{},{"id":1726,"data":1727,"type":543,"tunes":1729},"lrS5SZf7xo",{"text":1728},"The problem described here is not unique to games.",{},{"id":1731,"data":1732,"type":543,"tunes":1734},"5mVQ4gvMap",{"text":1733},"Gaming simply makes it particularly visible.",{},{"id":1736,"data":1737,"type":543,"tunes":1739},"sxDkAtFbN1",{"text":1738},"A player's question can frame the answer. A patch can invalidate previous knowledge. Missing state can change the correct action. A plausible explanation can survive because nobody searched for evidence against it.",{},{"id":1741,"data":1742,"type":543,"tunes":1744},"yIQuxKvgOq",{"text":1743},"The same reasoning failures appear in research, software engineering, architecture, strategy and technical diagnosis.",{},{"id":1746,"data":1747,"type":543,"tunes":1749},"8oqxxNDFev",{"text":1748},"I developed the broader methodology behind these ideas separately on stajic.de in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to a General AI Reasoning Framework\u003C\u002Fa>.",{},{"id":1751,"data":1752,"type":543,"tunes":1754},"z2sH7SFLlE",{"text":1753},"The core idea is simple:",{},{"id":1756,"data":1757,"type":554,"tunes":1759},"nl7PTWytfG",{"text":1758,"caption":13,"alignment":553},"A good AI system should not merely generate a convincing answer. It should know what the answer had to survive before becoming a conclusion.",{},{"id":1761,"data":1762,"type":561,"tunes":1764},"YK_Y28YwfD",{"text":1763,"level":47},"What Reliable Gaming AI Should Know",{},{"id":1766,"data":1767,"type":543,"tunes":1769},"fFPzGrmqKY",{"text":1768},"The most useful future gaming AI may therefore be defined less by how naturally it talks and more by how reliably it understands its epistemic position.",{},{"id":1771,"data":1772,"type":604,"tunes":1783},"8agIg9W4_V",{"meta":1773,"items":1774,"style":603},{},[1775,1776,1777,1778,1779,1780,1781,1782],"What game version am I reasoning about?","Which facts come directly from the current game state?","Which information came from documentation or retrieval?","Which parts of my answer are assumptions?","What does the player actually want to optimize?","What plausible alternative explanation exists?","What evidence would prove my current explanation wrong?","How certain should I be before recommending or performing an action?",{},{"id":1785,"data":1786,"type":543,"tunes":1788},"VYgvyTiLKY",{"text":1787},"Those questions are less spectacular than an NPC generating unlimited dialogue.",{},{"id":1790,"data":1791,"type":543,"tunes":1793},"CB3FXYU4N7",{"text":1792},"They may ultimately matter more.",{},{"id":1795,"data":1796,"type":554,"tunes":1798},"pvMTy0psDB",{"text":1797,"caption":13,"alignment":553},"The future of gaming AI is not only characters that can talk. It is agents that can distinguish what they know, what they assume and what the game can actually prove.",{},{"id":1800,"data":1801,"type":1006,"tunes":1802},"EkMunyL5KK",{},{},{"id":1804,"data":1805,"type":561,"tunes":1807},"TkN1CYiCcF",{"text":1806,"level":47},"Current Gaming AI Context",{},{"id":1809,"data":1810,"type":543,"tunes":1812},"9QazRlfh_5",{"text":1811},"Several current projects illustrate the transition described in this article. Google DeepMind's SIMA 2 integrates Gemini reasoning with perception and action across 3D virtual environments. NVIDIA ACE provides technologies for conversational and autonomous game characters, including local inference, contextual retrieval and action-oriented agent workflows. KRAFTON's PUBG Ally combines a small local language model with game-specific context and a separate fast-response behaviour system. Microsoft Research's Muse and WHAM investigate world models capable of generating or predicting gameplay from visual state and controller actions.",{},{"id":1814,"data":1815,"type":543,"tunes":1817},"aNGcSo0B4j",{"text":1816},"These systems have different objectives and should not be treated as implementations of the reasoning methodology proposed here. They demonstrate the broader architectural shift that makes the problem relevant: generative models are increasingly becoming active components of game-world interpretation and behaviour rather than external text generators.",{},{"id":1819,"data":1820,"type":561,"tunes":1822},"11490gZEFF",{"text":1821,"level":1019},"Selected References",{},{"id":1824,"data":1825,"type":604,"tunes":1829},"tpfjHRyLMj",{"meta":1826,"items":1827,"style":603},{},[1023,1828,1025,1026,1027],"NVIDIA — \u003Ci>ACE for Games\u003C\u002Fi> and NVIDIA Game Agent SDK documentation.",{},{"id":1831,"data":1832,"type":1006,"tunes":1833},"ECYNSajqTR",{},{},{"id":1835,"data":1836,"type":561,"tunes":1838},"OXo_hIY4FF",{"text":1837,"level":47},"Go Deeper: AI Reasoning Methodology",{},{"id":1840,"data":1841,"type":543,"tunes":1843},"ge2QljMsH8",{"text":1842},"For the complete domain-independent methodology behind evidence separation, competing hypotheses, Prompt Invariance, falsification and confidence calibration, continue with \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>\u003C\u002Fa> on stajic.de.",{},"2.31.6","Gaming AI is moving from scripted NPCs toward assistants and agents that can perceive, reason and act inside virtual worlds. But games create a difficult reasoning environment: patches change facts, player goals change what “best” means, game state changes the correct action, and confident AI explanations can hide missing context or false assumptions. Reliable gaming AI needs more than a better prompt.",{"lang":7,"title":534,"content":536,"contentJson":1847,"excerpt":1037},{"time":538,"blocks":1848,"version":1036},[1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1884,1886,1888,1890,1892,1894,1896,1898,1900,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2054,2056,2058,2060,2062,2064,2066,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2108,2110,2112,2114,2116,2118,2120,2122,2124,2127,2129,2131],{"data":1850,"type":543},{"text":542},{"data":1852,"type":543},{"text":546},{"data":1854,"type":543},{"text":549},{"data":1856,"type":554},{"text":552,"caption":13,"alignment":553},{"data":1858,"type":543},{"text":557},{"data":1860,"type":561},{"text":560,"level":47},{"data":1862,"type":543},{"text":564},{"data":1864,"type":543},{"text":567},{"data":1866,"type":543},{"text":570},{"data":1868,"type":543},{"text":573},{"data":1870,"type":543},{"text":576},{"data":1872,"type":543},{"text":579},{"data":1874,"type":554},{"text":582,"caption":13,"alignment":553},{"data":1876,"type":561},{"text":585,"level":47},{"data":1878,"type":543},{"text":588},{"data":1880,"type":543},{"text":591},{"data":1882,"type":604},{"items":1883,"style":603},[595,596,597,598,599,600,601,602],{"data":1885,"type":543},{"text":607},{"data":1887,"type":554},{"text":610,"caption":13,"alignment":553},{"data":1889,"type":561},{"text":613,"level":47},{"data":1891,"type":543},{"text":616},{"data":1893,"type":554},{"text":619,"caption":13,"alignment":553},{"data":1895,"type":543},{"text":622},{"data":1897,"type":543},{"text":625},{"data":1899,"type":543},{"text":628},{"data":1901,"type":604},{"items":1902,"style":603},[632,633,634,635,636,637,638,639,640,641,642],{"data":1904,"type":543},{"text":645},{"data":1906,"type":543},{"text":648},{"data":1908,"type":561},{"text":651,"level":47},{"data":1910,"type":543},{"text":654},{"data":1912,"type":554},{"text":657,"caption":13,"alignment":553},{"data":1914,"type":543},{"text":660},{"data":1916,"type":543},{"text":663},{"data":1918,"type":543},{"text":666},{"data":1920,"type":554},{"text":669,"caption":13,"alignment":553},{"data":1922,"type":543},{"text":672},{"data":1924,"type":543},{"text":675},{"data":1926,"type":561},{"text":678,"level":47},{"data":1928,"type":543},{"text":681},{"data":1930,"type":543},{"text":684},{"data":1932,"type":604},{"items":1933,"style":603},[688,689,690,691,692,693,694,695],{"data":1935,"type":543},{"text":698},{"data":1937,"type":554},{"text":701,"caption":13,"alignment":553},{"data":1939,"type":543},{"text":704},{"data":1941,"type":561},{"text":707,"level":47},{"data":1943,"type":543},{"text":710},{"data":1945,"type":554},{"text":713,"caption":13,"alignment":553},{"data":1947,"type":543},{"text":716},{"data":1949,"type":543},{"text":719},{"data":1951,"type":561},{"text":722,"level":47},{"data":1953,"type":543},{"text":725},{"data":1955,"type":554},{"text":728,"caption":13,"alignment":553},{"data":1957,"type":543},{"text":731},{"data":1959,"type":543},{"text":734},{"data":1961,"type":604},{"items":1962,"style":603},[738,739,740,741,742],{"data":1964,"type":543},{"text":745},{"data":1966,"type":543},{"text":748},{"data":1968,"type":554},{"text":751,"caption":13,"alignment":553},{"data":1970,"type":561},{"text":754,"level":47},{"data":1972,"type":543},{"text":757},{"data":1974,"type":554},{"text":760,"caption":13,"alignment":553},{"data":1976,"type":543},{"text":763},{"data":1978,"type":543},{"text":766},{"data":1980,"type":543},{"text":769},{"data":1982,"type":543},{"text":772},{"data":1984,"type":561},{"text":775,"level":47},{"data":1986,"type":543},{"text":778},{"data":1988,"type":543},{"text":781},{"data":1990,"type":604},{"items":1991,"style":603},[785,786,787,788],{"data":1993,"type":543},{"text":791},{"data":1995,"type":543},{"text":794},{"data":1997,"type":543},{"text":797},{"data":1999,"type":561},{"text":800,"level":47},{"data":2001,"type":543},{"text":803},{"data":2003,"type":543},{"text":806},{"data":2005,"type":543},{"text":809},{"data":2007,"type":543},{"text":812},{"data":2009,"type":554},{"text":815,"caption":13,"alignment":553},{"data":2011,"type":543},{"text":818},{"data":2013,"type":561},{"text":821,"level":47},{"data":2015,"type":543},{"text":824},{"data":2017,"type":543},{"text":827},{"data":2019,"type":604},{"items":2020,"style":603},[831,832,833,834,835,836,837,838,839,840,841],{"data":2022,"type":543},{"text":844},{"data":2024,"type":554},{"text":847,"caption":13,"alignment":553},{"data":2026,"type":543},{"text":850},{"data":2028,"type":561},{"text":853,"level":47},{"data":2030,"type":543},{"text":856},{"data":2032,"type":554},{"text":859,"caption":13,"alignment":553},{"data"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erfolgreich abgerufen",{"items":2135,"source":2199,"manualIds":2200,"manualMatchedIds":2201},[2136,2142,2147,2152,2158,2163,2168,2174,2179,2184,2189,2194],{"id":2137,"slug":2138,"title":2139,"excerpt":2140,"featuredImage":14,"publishedAt":2141},"45","amiibo-as-culture-ritual-play-value-and-collecting-standards","amiibo en tant que culture : rituel, valeur ludique et normes de collection","Les Amiibo sont de tout petits objets dotés d'une grande culture. Ce pilier relie valeur ludique, rituels de collection, état et normes à long terme.","2026-02-19T11:00:00.000Z",{"id":2143,"slug":2144,"title":2145,"excerpt":2146,"featuredImage":14,"publishedAt":2141},"10","why-feel-beats-specs-a-value-doctrine-for-gaming","Pourquoi le ressenti l'emporte sur les caractéristiques : une doctrine de la valeur pour le gaming","Les caractéristiques sont faciles à commercialiser. Le ressenti est difficile à construire. C'est la doctrine qui garde votre configuration honnête et vos améliorations intentionnelles.",{"id":2148,"slug":2149,"title":2150,"excerpt":2151,"featuredImage":14,"publishedAt":2141},"41","rare-games-real-value-collecting-without-the-price-race","Jeux rares, valeur réelle : collectionner sans la course aux prix","Un guide de référence sur les jeux rares à travers la culture et les standards : quoi collectionner, comment évaluer et comment éviter les regrets liés au battage médiatique.",{"id":2153,"slug":2154,"title":2155,"excerpt":2156,"featuredImage":14,"publishedAt":2157},"428","best-amiibo-per-game-practical-pairings-across-nintendo-titles","Meilleurs amiibo par jeu : associations pratiques à travers les titres Nintendo","Les figurines amiibo interagissent avec de nombreux titres Nintendo, pourtant l'utilité de chaque figurine varie selon le jeu spécifique. Certaines débloquent des objets, d'autres stockent des données, d'autres encore déclenchent simplement de petits bonus. Au fil du temps, une tendance s'est dessinée. Certaines figurines apparaissent de manière répétée dans les discussions concernant des jeux particuliers, non pas parce qu'elles sont rares ou de collection, mais parce que l'interaction en jeu semble plus notable. Cet aperçu examine ces associations couramment mentionnées.","2026-03-12T19:37:00.000Z",{"id":2159,"slug":2160,"title":2161,"excerpt":2162,"featuredImage":14,"publishedAt":2141},"44","the-no-noise-review-standard-how-to-read-tech-without-getting-played","La norme de test sans bruit : comment lire la tech sans se faire avoir","Un guide de référence pour lire les critiques : séparez les arguments marketing des résultats, et ignorez les indicateurs qui ne prédisent pas votre expérience.",{"id":2164,"slug":2165,"title":2166,"excerpt":2167,"featuredImage":14,"publishedAt":2141},"39","culture-over-hype-why-better-beats-more","Culture au-dessus du battage médiatique : Pourquoi « Mieux » bat « Plus »","L'internet récompense la rapidité et le volume. Nous choisissons les standards et la culture : moins d'articles, de meilleurs choix, une valeur à long terme plus forte.",{"id":2169,"slug":2170,"title":2171,"excerpt":2172,"featuredImage":14,"publishedAt":2173},"291","gameplay-vs-collecting-value-when-an-amiibo-is-worth-buying-and-when-it-isnt","Gameplay vs Valeur de collection : Quand un amiibo vaut la peine d'être acheté (et quand ce n'est pas le cas)","Les amiibo peuvent en valoir la peine pour le gameplay, la collection ou les deux — mais les raisons sont différentes. Utilisez ce cadre pour décider sans céder à l'engouement ni aux regrets.","2026-02-21T12:00:00.000Z",{"id":2175,"slug":2176,"title":2177,"excerpt":2178,"featuredImage":14,"publishedAt":2141},"66","experience-first-why-gaming-feel-beats-specs-a-value-standard","L'expérience d'abord : Pourquoi le ressenti de jeu surpasse les caractéristiques (Un standard de valeur)","Internet vend des chiffres. Les joueurs ressentent le timing. C'est un standard de valeur pratique : l'expérience d'abord, la stabilité d'abord, puis le matériel et le prix.",{"id":2180,"slug":2181,"title":2182,"excerpt":2183,"featuredImage":14,"publishedAt":2141},"47","rare-game-condition-grading-cib-sealed-loose-a-practical-standard","Évaluation de l'état des jeux rares : CIB, scellé, loose (un standard pratique)","Un standard d'évaluation pratique pour les jeux rares : CIB vs scellé vs loose, ce qu'il faut vérifier, comment décrire l'état honnêtement et comment éviter les regrets.",{"id":2185,"slug":2186,"title":2187,"excerpt":2188,"featuredImage":14,"publishedAt":2141},"46","the-portal-map-how-to-use-figure-rocks-without-wasting-time","La carte du portail : comment utiliser figure.rocks sans perdre de temps","Une page de navigation rapide par piliers : par où commencer, comment résoudre les problèmes, comment choisir son équipement et comment collectionner selon des normes.",{"id":2190,"slug":2191,"title":2192,"excerpt":2193,"featuredImage":14,"publishedAt":2141},"67","start-here-how-to-use-figure-rocks-feel-fix-gear-craft","Commencez ici : Comment utiliser Figure.Rocks (Ressenti, Correction, Équipement, Savoir-faire)","Une façon simple de naviguer sur le portail : comprendre le ressenti, appliquer des corrections, choisir son équipement par l'expérience et maintenir des standards plus élevés que le bruit d'Internet.",{"id":2195,"slug":2196,"title":2197,"excerpt":2198,"featuredImage":14,"publishedAt":2141},"50","authenticity-and-provenance-avoid-fakes-rewraps-and-bad-listings","Authenticité & Provenance : Éviter les contrefaçons, les réemballages et les mauvaises annonces","Une norme pratique pour réduire les risques lors de l'achat de jeux rares : comment évaluer l'authenticité, la provenance et la qualité de l'annonce sans paranoïa.","fallback",[],[]]