PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913847249469440 |
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| author | Zhu, Qinglin Zhao, Runcong Liang, Bin Du, Jinhua Gui, Lin He, Yulan |
| author_facet | Zhu, Qinglin Zhao, Runcong Liang, Bin Du, Jinhua Gui, Lin He, Yulan |
| contents | We introduce WellPlay, a reasoning dataset for multi-agent conversational inference in Murder Mystery Games (MMGs). WellPlay comprises 1,482 inferential questions across 12 games, spanning objectives, reasoning, and relationship understanding, and establishes a systematic benchmark for evaluating agent reasoning abilities in complex social settings. Building on this foundation, we present PLAYER*, a novel framework for Large Language Model (LLM)-based agents in MMGs. MMGs pose unique challenges, including undefined state spaces, absent intermediate rewards, and the need for strategic reasoning through natural language. PLAYER* addresses these challenges with a sensor-based state representation and an information-driven strategy that optimises questioning and suspect pruning. Experiments show that PLAYER* outperforms existing methods in reasoning accuracy, efficiency, and agent-human interaction, advancing reasoning agents for complex social scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17662 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games Zhu, Qinglin Zhao, Runcong Liang, Bin Du, Jinhua Gui, Lin He, Yulan Computation and Language We introduce WellPlay, a reasoning dataset for multi-agent conversational inference in Murder Mystery Games (MMGs). WellPlay comprises 1,482 inferential questions across 12 games, spanning objectives, reasoning, and relationship understanding, and establishes a systematic benchmark for evaluating agent reasoning abilities in complex social settings. Building on this foundation, we present PLAYER*, a novel framework for Large Language Model (LLM)-based agents in MMGs. MMGs pose unique challenges, including undefined state spaces, absent intermediate rewards, and the need for strategic reasoning through natural language. PLAYER* addresses these challenges with a sensor-based state representation and an information-driven strategy that optimises questioning and suspect pruning. Experiments show that PLAYER* outperforms existing methods in reasoning accuracy, efficiency, and agent-human interaction, advancing reasoning agents for complex social scenarios. |
| title | PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.17662 |