mlsanityData to 14 Oct 2026

CogniPlay: a work-in-progress Human-like model for General Game Playing

arXiv 2507.05868 ↗First posted 8 Jul 2025cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 8 Jul 2025.

Builds on

11 of 41 references.

References · 11

  1. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  2. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 349
  3. 2017Thinking Fast and Slow with Deep Learning and Tree Searchcited by 252
  4. 2020Aligning Superhuman AI with Human Behavior: Chess as a Model Systemcited by 79
  5. 2023Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agentscited by 77
  6. 2019Ludii -- The Ludemic General Game Systemcited by 39
  7. 2024Planning Like Human: A Dual-process Framework for Dialogue Planningcited by 32
  8. 2023Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Gamescited by 11
  9. 2020Modern Techniques for Ancient Gamescited by 9
  10. 2022Spatial State-Action Features for General Gamescited by 3
  11. 2019Foundations of Digital Archaeoludologycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Toward Modeling Player-Specific Chess Behaviorscited by 1