Multi-party Discussion by AI Agents Leveraging the Systematics of Turn-taking

Abstract

LLM (Large language model)-based multi-agent systems face significant challenges in achieving human-like natural dialogue. Existing systems rely on simplistic turn-taking models, failing to adequately reproduce the nuanced social interactions inherent in human conversations. This study proposes the Murder Mystery Agents (MMAgents) framework, implementing principles of conversational turn-taking discovered in conversation analysis research with a speaker selection mechanism based on adjacency pairs and turn-taking, and a self-selection method considering agents' internal states. Through experiments using a murder mystery game setting, it was confirmed that the dialogue coherence and reasoning capabilities were substantially improved. The experimental results also demonstrate reduced dialogue breakdowns and enhanced information sharing, offering novel design guidelines for multi-agent dialogue systems that incorporate human conversational norms.