{"title":"MultiStyle: Characterizing Multiplayer Cooperative Gameplay by Incorporating Distinct Player Playstyles in a Multi-Agent Planner","authors":"Eric W. Lang, R. Michael Young","doi":"10.1609/aiide.v19i1.27505","DOIUrl":null,"url":null,"abstract":"This paper presents MultiStyle, a multi-agent centralized heuristic search planner that incorporates distinct agent playstyles to generate solution plans where characters express individual preferences while cooperating to reach a goal. We include algorithmic details, an example domain, and multiple different solution plans generated with unique agent playstyle sets. We discuss our intent to incorporate this planner in a tool for game level designers to help them anticipate and understand how teams of players with distinct playstyles may play through their levels. Ultimately, MultiStyle generates solution plans with a novel and increased expressive range by attempting to satisfy sets of action and proposition preferences for each agent.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aiide.v19i1.27505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents MultiStyle, a multi-agent centralized heuristic search planner that incorporates distinct agent playstyles to generate solution plans where characters express individual preferences while cooperating to reach a goal. We include algorithmic details, an example domain, and multiple different solution plans generated with unique agent playstyle sets. We discuss our intent to incorporate this planner in a tool for game level designers to help them anticipate and understand how teams of players with distinct playstyles may play through their levels. Ultimately, MultiStyle generates solution plans with a novel and increased expressive range by attempting to satisfy sets of action and proposition preferences for each agent.