A Survey on Large Language Model-Based Game Agents

• URL: http://arxiv.org/abs/2404.02039v1
• Date: Tue, 2 Apr 2024 15:34:18 GMT
• Title: A Survey on Large Language Model-Based Game Agents
• Authors: Sihao Hu, Tiansheng Huang, Fatih Ilhan, Selim Tekin, Gaowen Liu, Ramana Kompella, Ling Liu,
• Abstract summary: The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI) This paper provides a comprehensive overview of LLM-based game agents from a holistic viewpoint.
• Score: 9.892954815419452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI). The progress of LLMs and their multimodal counterparts (MLLMs) offers an unprecedented opportunity to evolve and empower game agents with human-like decision-making capabilities in complex computer game environments. This paper provides a comprehensive overview of LLM-based game agents from a holistic viewpoint. First, we introduce the conceptual architecture of LLM-based game agents, centered around six essential functional components: perception, memory, thinking, role-playing, action, and learning. Second, we survey existing representative LLM-based game agents documented in the literature with respect to methodologies and adaptation agility across six genres of games, including adventure, communication, competition, cooperation, simulation, and crafting & exploration games. Finally, we present an outlook of future research and development directions in this burgeoning field. A curated list of relevant papers is maintained and made accessible at: https://github.com/git-disl/awesome-LLM-game-agent-papers.

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