[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-145549-105":59,"doc-detail-145549-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","learning-to-discuss-strategically-a-case-study-on-one-night-ultimate-werewolf","Learning to Discuss Strategically - A Case Study on One Night Ultimate Werewolf","","Communication is central to human societies, enabling the exchange of information and beliefs. While large language models can form agents for many tasks, many recent agents lack explicit control over discussion tactics that are critical in communication games. One Night Ultimate Werewolf (ONUW) demands strategic discussion policies because role changes increase uncertainty and complexity. The study proves Perfect Bayesian Equilibria existence in ONUW with and without discussion, then proposes an RL-instructed language agent framework to learn effective discussion tactics and demonstrates strong results and generalizability across settings.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/learning-to-discuss-strategically-a-case-study-on-one-night-ultimate-werewolf/145549/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/learning-to-discuss-strategically-a-case-study-on-one-night-ultimate-werewolf/145549.png","ImageObject",300,407,{"name":92,"@type":93},"Cipher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-08-26",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why does the ONUW game require strategic discussion?","Question",{"text":112,"@type":113},"Role changes during the game increase uncertainty and complexity, so players must rely on discussions to infer final roles and adjust beliefs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What do the authors analyze regarding equilibria in ONUW?",{"text":117,"@type":113},"They establish the existence of Perfect Bayesian Equilibria in two scenarios: with discussion and without discussion.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the proposed framework decide discussion tactics?",{"text":121,"@type":113},"It uses a language agent whose discussion policy is trained via reinforcement learning to select appropriate tactics based on current observations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},145549,1787724678,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},687208528416,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","arXiv :2405 . 19946v2 [ cs .AI] 12 Jan 2025  \nLearning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf  \nXuanfa Jin∗, 1 ,3 , Ziyan Wang∗ ,2 , Yali Du†,2 , Meng Fang4 , Haifeng Zhang†, 1 ,3 ,5 , Jun Wang†,6  \n1Institute of Automation, Chinese Academy of Sciences, 2 Cooperative AI Lab, Department of Informatics, King’s College London, 3 School of Artificial Intelligence, University of Chinese Academy of Sciences, 4University of Liverpool, 5Nanjing Artificial Intelligence Research of IA, 6AI Centre, Department of Computer Science, UCL  \nAbstract  \nCommunication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games.  \nAs a variant of the famous communication game Werewolf, One Night Ultimate Werewolf (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game. In this work, we first present the existence of the Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with discussion and one without. The results showcase that the discussion greatly changes players’ utilities by affecting their beliefs, emphasizing the significance of discussion tactics. Based on the insights obtained from the analyses, we propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt. Our experimental results on several ONUW game settings demonstrate the effectiveness and generalizability of our proposed framework. The project page of our paper:  \n[one-night-ultimate-werewolf.github.io](one-night-ultimate-werewolf.github.io).  \n1 Introduction  \nMany games such as StarCraft [1, 2], Diplomacy [3, 4] can approximate various fundamental issues in real life, and studying these games contributes to a better understanding of the functioning of our society [5] . Designing artificial intelligence (AI) agents that can play these games well has attracted a lot of attention [6, 2, 3] . Fortunately, recent large language models (LLMs) have demonstrated significant potential in constructing intelligent agents in numerous tasks [7–10] due to their impressive reasoning and emergent generalization abilities [11–13] . Moreover, LLM-based agents have achieved approaching or even surpassing human performance in games like Chess[14], Minecraft [15–17], Avalon [18–20] and Werewolf [21–23], etc.  \nIn the game Werewolf [24], the hidden roles and uncertain discussions greatly influence the gameplay, making it challenging for players. Compared to it, the One Night Ultimate Werewolf (ONUW) game adds roles that can change the roles of players, and all players only have one nighttime to take action  \nand one daytime to discuss and vote. Therefore, the key challenge of the ONUW game is to deduce ∗Equal contribution. Author ordering is determined by coin flip.  \n†Correspondence to: 〈[yali.du@kcl.ac.uk](yali.du@kcl.ac.uk)〉, 〈[haifeng.zhang@ia.ac.cn](haifeng.zhang@ia.ac.cn)〉, 〈[jun.wang@cs.ucl.ac.uk](jun.wang@cs.ucl.ac.uk)〉 .  \n38th Conference on Neural Information Processing Systems (NeurIPS 2024) .  \nand distinguish the final roles of all players from their statements. Meanwhile, due to the sequential execution of actions at night, the information obtained by the former player might be not reliable, leading to the rise of reasoning difficulty and uncertainty. For example, Seer and Robber are two roles in the ONUW game with special abilities, and the Seer saw a player was a Werewolf at night. However, if the Robber switched that player’s role later on, the information obtained by Seer becomes invalid. So players need to discuss strategically, leveraging others’ statements and their ow","cbCais4Z3cUZApis","https://ap.wps.com/l/cbCais4Z3cUZApis","pdf",876552,31,"English","# Abstract\n# Introduction\n## Challenges in ONUW gameplay\n## Strategic discussion and agent control\n## Contributions and proposed framework","[{\"question\":\"Why does the ONUW game require strategic discussion?\",\"answer\":\"Role changes during the game increase uncertainty and complexity, so players must rely on discussions to infer final roles and adjust beliefs.\"},{\"question\":\"What do the authors analyze regarding equilibria in ONUW?\",\"answer\":\"They establish the existence of Perfect Bayesian Equilibria in two scenarios: with discussion and without discussion.\"},{\"question\":\"How does the proposed framework decide discussion tactics?\",\"answer\":\"It uses a language agent whose discussion policy is trained via reinforcement learning to select appropriate tactics based on current observations.\"}]","Learning to Discuss Strategically - A Case Study on One Night Ultimate Werewolf | PDF",78]