{"title":"Improved decisions for unknown behaviours in interactive dynamic influence diagrams","authors":"Yinghui Pan, Mengen Zhou, Biyang Ma, Yifeng Zeng, Yew-soon Ong, Guoquan Liu","doi":"10.1007/s10462-025-11355-y","DOIUrl":null,"url":null,"abstract":"<div><p>Interactive dynamic influence diagrams (I-DIDs) are a general decision framework for a subject agent who interacts with other agents (of either collaborative or competitive) in a common environment with partial observability. The subject agent aims to optimize its decision-making (response strategy) while other agents concurrently adapt their behaviors over time. The I-DID model has faced a long-term challenge when other agents exhibit unknown behaviors that go beyond what the subject agent has planned for prior to their interactions. This is because the subject agent does not hold the capability of modeling unknown behaviours of other agents in traditional I-DID techniques. In this article, we adapt two different swarm intelligence (SI) techniques to develop new behaviours for other agents in I-DIDs. The SI-based algorithms have the strength of generating a collective set of behaviours that could potentially contain various types of agents’ behaviours. We theoretically analyze how the two algorithms impact the subject agent’s decision quality, and empirically demonstrate the algorithm performance in two commonly used problem domains.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11355-y.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-025-11355-y","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Interactive dynamic influence diagrams (I-DIDs) are a general decision framework for a subject agent who interacts with other agents (of either collaborative or competitive) in a common environment with partial observability. The subject agent aims to optimize its decision-making (response strategy) while other agents concurrently adapt their behaviors over time. The I-DID model has faced a long-term challenge when other agents exhibit unknown behaviors that go beyond what the subject agent has planned for prior to their interactions. This is because the subject agent does not hold the capability of modeling unknown behaviours of other agents in traditional I-DID techniques. In this article, we adapt two different swarm intelligence (SI) techniques to develop new behaviours for other agents in I-DIDs. The SI-based algorithms have the strength of generating a collective set of behaviours that could potentially contain various types of agents’ behaviours. We theoretically analyze how the two algorithms impact the subject agent’s decision quality, and empirically demonstrate the algorithm performance in two commonly used problem domains.
期刊介绍:
Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.