Periklis Mantenoglou, Manolis Pitsikalis, A. Artikis
{"title":"Stream Reasoning with Cycles","authors":"Periklis Mantenoglou, Manolis Pitsikalis, A. Artikis","doi":"10.24963/kr.2022/56","DOIUrl":null,"url":null,"abstract":"Temporal specifications, such as those found in multi-agent systems, often include cyclic dependencies. Moreover, there is an increasing need to evaluate such specifications in an online manner, upon streaming data. Consider, for example, the online computation of the normative positions of the agents engaging in an e-commerce protocol. We present a formal computational framework that deals with cyclic dependencies in an efficient way. Moreover, we demonstrate the effectiveness of our framework on large synthetic and real data streams, from the fields of multi-agent systems and composite event recognition.","PeriodicalId":351970,"journal":{"name":"Proceedings of the Nineteenth International Conference on Principles of Knowledge Representation and Reasoning","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Nineteenth International Conference on Principles of Knowledge Representation and Reasoning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24963/kr.2022/56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Temporal specifications, such as those found in multi-agent systems, often include cyclic dependencies. Moreover, there is an increasing need to evaluate such specifications in an online manner, upon streaming data. Consider, for example, the online computation of the normative positions of the agents engaging in an e-commerce protocol. We present a formal computational framework that deals with cyclic dependencies in an efficient way. Moreover, we demonstrate the effectiveness of our framework on large synthetic and real data streams, from the fields of multi-agent systems and composite event recognition.