Summary of A Survey on Large Language Model-based Social Agents in Game-theoretic Scenarios, by Xiachong Feng et al.
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
by Xiachong Feng, Longxu Dou, Ella Li, Qinghao Wang, Haochuan Wang, Yu Guo, Chang Ma, Lingpeng Kong
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive review of existing research on Large Language Model (LLM)-based social agents in game-theoretic scenarios. The study organizes findings into three core components: Game Framework, Social Agent, and Evaluation Protocol. The game framework covers diverse scenarios, including choice-focusing and communication-focusing games. The social agent part explores agents’ preferences, beliefs, and reasoning abilities. The evaluation protocol includes both game-agnostic and game-specific metrics for assessing agent performance. This survey aims to advance the development and evaluation of social agents in game-theoretic scenarios by reflecting on current research and identifying future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can be programmed to think like humans when playing games or making decisions with other people. Researchers have been studying this topic, but there hasn’t been a big review of all the work done so far. This study takes a step back and looks at what’s already been discovered. It groups the findings into three main areas: the types of games being played, how computers make decisions, and how we measure their success. The goal is to help us better understand and improve how computers interact with humans in these situations. |
Keywords
» Artificial intelligence » Large language model
