Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery games
IntroductionMulti-agent systems utilizing large language models (LLMs) have shown great promise in achieving natural dialogue. However, smooth dialogue control and autonomous decision making among agents still remain challenging.MethodsIn this study, we focus on conversational norms such as adjacenc...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1582287/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849421831883194368 |
|---|---|
| author | Ryota Nonomura Hiroki Mori |
| author_facet | Ryota Nonomura Hiroki Mori |
| author_sort | Ryota Nonomura |
| collection | DOAJ |
| description | IntroductionMulti-agent systems utilizing large language models (LLMs) have shown great promise in achieving natural dialogue. However, smooth dialogue control and autonomous decision making among agents still remain challenging.MethodsIn this study, we focus on conversational norms such as adjacency pairs and turn-taking found in conversation analysis and propose a new framework called “Murder Mystery Agents” that applies these norms to AI agents' dialogue control. As an evaluation target, we employed the “Murder Mystery” game, a reasoning-type table-top role-playing game that requires complex social reasoning and information manipulation. The proposed framework integrates next speaker selection based on adjacency pairs and a self-selection mechanism that takes agents' internal states into account to achieve more natural and strategic dialogue.ResultsTo verify the effectiveness of this new approach, we analyzed utterances that led to dialogue breakdowns and conducted automatic evaluation using LLMs, as well as human evaluation using evaluation criteria developed for the Murder Mystery game. Experimental results showed that the implementation of the next speaker selection mechanism significantly reduced dialogue breakdowns and improved the ability of agents to share information and perform logical reasoning.DiscussionThe results of this study demonstrate that the systematics of turn-taking in human conversation are also effective in controlling dialogue among AI agents, and provide design guidelines for more advanced multi-agent dialogue systems. |
| format | Article |
| id | doaj-art-ae63fa62143249cc8677a3b66e85759d |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-ae63fa62143249cc8677a3b66e85759d2025-08-20T03:31:21ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-06-01810.3389/frai.2025.15822871582287Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery gamesRyota NonomuraHiroki MoriIntroductionMulti-agent systems utilizing large language models (LLMs) have shown great promise in achieving natural dialogue. However, smooth dialogue control and autonomous decision making among agents still remain challenging.MethodsIn this study, we focus on conversational norms such as adjacency pairs and turn-taking found in conversation analysis and propose a new framework called “Murder Mystery Agents” that applies these norms to AI agents' dialogue control. As an evaluation target, we employed the “Murder Mystery” game, a reasoning-type table-top role-playing game that requires complex social reasoning and information manipulation. The proposed framework integrates next speaker selection based on adjacency pairs and a self-selection mechanism that takes agents' internal states into account to achieve more natural and strategic dialogue.ResultsTo verify the effectiveness of this new approach, we analyzed utterances that led to dialogue breakdowns and conducted automatic evaluation using LLMs, as well as human evaluation using evaluation criteria developed for the Murder Mystery game. Experimental results showed that the implementation of the next speaker selection mechanism significantly reduced dialogue breakdowns and improved the ability of agents to share information and perform logical reasoning.DiscussionThe results of this study demonstrate that the systematics of turn-taking in human conversation are also effective in controlling dialogue among AI agents, and provide design guidelines for more advanced multi-agent dialogue systems.https://www.frontiersin.org/articles/10.3389/frai.2025.1582287/fullturn-takingconversation analysisgenerative AILLM-based agentmulti-party conversation |
| spellingShingle | Ryota Nonomura Hiroki Mori Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery games Frontiers in Artificial Intelligence turn-taking conversation analysis generative AI LLM-based agent multi-party conversation |
| title | Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery games |
| title_full | Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery games |
| title_fullStr | Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery games |
| title_full_unstemmed | Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery games |
| title_short | Who speaks next? Multi-party AI discussion leveraging the systematics of turn-taking in Murder Mystery games |
| title_sort | who speaks next multi party ai discussion leveraging the systematics of turn taking in murder mystery games |
| topic | turn-taking conversation analysis generative AI LLM-based agent multi-party conversation |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1582287/full |
| work_keys_str_mv | AT ryotanonomura whospeaksnextmultipartyaidiscussionleveragingthesystematicsofturntakinginmurdermysterygames AT hirokimori whospeaksnextmultipartyaidiscussionleveragingthesystematicsofturntakinginmurdermysterygames |