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A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges

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arxiv 2403.10249 v1 pith:H3TLQEM5 submitted 2024-03-15 cs.AI

Xinrun Xu , Yuxin Wang , Chaoyi Xu , Ziluo Ding , Jiechuan Jiang , Zhiming Ding , Börje F. Karlsson This is my paper · ORCID
classification cs.AI
keywords challengesagentsgamegamesimpactfulinterestmodelsplaying
verification ladder T0 review T1 audit T2 compute T3 formal
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The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry. But despite the surge in interest in this rapidly evolving area, there are scarce systematic reviews on their capabilities and potential in distinct impactful scenarios. This paper endeavours to help bridge this gap, offering a thorough examination of the current landscape of LM usage in regards to complex game playing scenarios and the challenges still open. Here, we seek to systematically review the existing architectures of LM-based Agents (LMAs) for games and summarize their commonalities, challenges, and any other insights. Furthermore, we present our perspective on promising future research avenues for the advancement of LMs in games. We hope to assist researchers in gaining a clear understanding of the field and to generate more interest in this highly impactful research direction. A corresponding resource, continuously updated, can be found in our GitHub repository.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning

    cs.AI 2025-08 conditional novelty 6.0 of 10

    A unified visual benchmark of six physics environments shows current VLMs, including frontier models, mostly fail at interactive physical reasoning and cannot convert descriptive physics knowledge into reliable control.

  2. Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details

    cs.AI 2026-08 conditional novelty 5.0 of 10

    For Other-Play in Yokai, agents trained with different implementation details coordinate across implementations about as well as across seeds, supporting inter-seed cross-play as a proxy for cross-implementation evaluation.

  3. Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A retrieve-summarize-extract pipeline with simple table-to-text serialization improves LLM extraction from hybrid long documents, and a new financial KPI dataset (FINE) is introduced to support evaluation.

  4. A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios

    cs.CL 2024-12 conditional novelty 3.0 of 10

    LLM-based game-playing agents are surveyed across choice-focused and communication-focused games, with a comparative performance table and future directions.

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