CogniPlay: a work-in-progress Human-like model for General Game Playing
- URL: http://arxiv.org/abs/2507.05868v1
- Date: Tue, 08 Jul 2025 10:48:29 GMT
- Title: CogniPlay: a work-in-progress Human-like model for General Game Playing
- Authors: Aloïs Rautureau, Éric Piette,
- Abstract summary: This paper presents an overview of findings from cognitive psychology and previous efforts to model human-like behavior in artificial agents.<n>It discusses their applicability to General Game Playing (GGP) and introduces our work-in-progress model based on these observations: CogniPlay.
- Score: 0.5524804393257919
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While AI systems have equaled or surpassed human performance in a wide variety of games such as Chess, Go, or Dota 2, describing these systems as truly "human-like" remains far-fetched. Despite their success, they fail to replicate the pattern-based, intuitive decision-making processes observed in human cognition. This paper presents an overview of findings from cognitive psychology and previous efforts to model human-like behavior in artificial agents, discusses their applicability to General Game Playing (GGP) and introduces our work-in-progress model based on these observations: CogniPlay.
Related papers
- JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions [75.42526766746515]
We propose a new commonsense reasoning dataset based on human's Interactive Fiction (IF) gameplay walkthroughs.
Our dataset focuses on the assessment of functional commonsense knowledge rules rather than factual knowledge.
Experiments show that the introduced dataset is challenging to previous machine reading models as well as the new large language models.
arXiv Detail & Related papers (2022-10-18T19:20:53Z) - Generative Personas That Behave and Experience Like Humans [3.611888922173257]
generative AI agents attempt to imitate particular playing behaviors represented as rules, rewards, or human demonstrations.
We extend the notion of behavioral procedural personas to cater for player experience, thus examining generative agents that can both behave and experience their game as humans would.
Our findings suggest that the generated agents exhibit distinctive play styles and experience responses of the human personas they were designed to imitate.
arXiv Detail & Related papers (2022-08-26T12:04:53Z) - Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
This work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior.
Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents.
arXiv Detail & Related papers (2022-08-22T14:06:06Z) - Detecting Individual Decision-Making Style: Exploring Behavioral
Stylometry in Chess [4.793072503820555]
We present a transformer-based approach to behavioral stylometry in the context of chess.
Our method operates in a few-shot classification framework, and can correctly identify a player from among thousands of candidate players.
We consider more broadly what our resulting embeddings reveal about human style in chess, as well as the potential ethical implications.
arXiv Detail & Related papers (2022-08-02T11:18:16Z) - WinoGAViL: Gamified Association Benchmark to Challenge
Vision-and-Language Models [91.92346150646007]
In this work, we introduce WinoGAViL: an online game to collect vision-and-language associations.
We use the game to collect 3.5K instances, finding that they are intuitive for humans but challenging for state-of-the-art AI models.
Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills.
arXiv Detail & Related papers (2022-07-25T23:57:44Z) - Modeling Human Behavior Part I -- Learning and Belief Approaches [0.0]
We focus on techniques which learn a model or policy of behavior through exploration and feedback.
Next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams.
arXiv Detail & Related papers (2022-05-13T07:33:49Z) - Playing for 3D Human Recovery [88.91567909861442]
In this work, we obtain massive human sequences by playing the video game with automatically annotated 3D ground truths.
Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine.
A simple frame-based baseline trained on GTA-Human outperforms more sophisticated methods by a large margin.
arXiv Detail & Related papers (2021-10-14T17:49:42Z) - Human-Level Reinforcement Learning through Theory-Based Modeling,
Exploration, and Planning [27.593497502386143]
Theory-Based Reinforcement Learning uses human-like intuitive theories to explore and model an environment.
We instantiate the approach in a video game playing agent called EMPA.
EMPA matches human learning efficiency on a suite of 90 Atari-style video games.
arXiv Detail & Related papers (2021-07-27T01:38:13Z) - Teach me to play, gamer! Imitative learning in computer games via
linguistic description of complex phenomena and decision tree [55.41644538483948]
We present a new machine learning model by imitation based on the linguistic description of complex phenomena.
The method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.
arXiv Detail & Related papers (2021-01-06T21:14:10Z) - Mastering Atari with Discrete World Models [61.7688353335468]
We introduce DreamerV2, a reinforcement learning agent that learns behaviors purely from predictions in the compact latent space of a powerful world model.
DreamerV2 constitutes the first agent that achieves human-level performance on the Atari benchmark of 55 tasks by learning behaviors inside a separately trained world model.
arXiv Detail & Related papers (2020-10-05T17:52:14Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.