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AI Research
arXiv CS.AI·Jun 9·5 min read
A Survey on Large Language Model-Based Game Agents
TL;DR
Game agents have traditionally struggled with generalizable reasoning and adaptability in complex environments. This survey reviews Large Language Model-based Game Agents (LLMGAs), which leverage the capabilities of LLMs to enhance reasoning and memory.
✦ Why It Matters
Engineers can leverage LLMs to build more adaptable and intelligent game agents, enhancing AI applications.
Key Takeaways
- Explore LLMs for improved game agent capabilities.
- Utilize memory and reasoning components in agent design.
- Implement communication protocols for multi-agent coordination.
- Adapt agent designs to specific game genre requirements.
How It Works
LLMGAs leverage LLMs to process language, enabling agents to understand and interact with their environments. The architecture integrates memory systems for retaining information, reasoning capabilities for decision-making, and perception-action interfaces that allow agents to respond to stimuli effectively.