{"type":"ScholarlyArticle","title":"Order-Dependent Event Models for Agent Interactions","authors":[{"name":"Debarun Bhattacharjya","slug":"debarun-bhattacharjya","given":"Debarun","family":"Bhattacharjya","affiliation":"Research AI , IBM T. J. Watson Research Center"},{"name":"Tian Gao","slug":"tian-gao","given":"Tian","family":"Gao","affiliation":"Research AI , IBM T. J. Watson Research Center"},{"name":"Dharmashankar Subramanian","slug":"dharmashankar-subramanian","given":"Dharmashankar","family":"Subramanian","affiliation":"Research AI , IBM T. J. Watson Research Center"}],"abstract":"In multivariate event data, the instantaneous rate of an event's occurrence may be sensitive to the temporal sequence in which other influencing events have occurred in the history. For example, an agent’s actions are typically driven by preceding actions taken by the agent as well as those of other relevant agents in some order. We introduce a novel statistical/causal model for capturing such an order-sensitive historical dependence, where an event’s arrival rate is determined by the order in which its underlying causal events have occurred in the recent past. We propose an algorithm to discover these causal events and learn the most influential orders using time-stamped event occurrence data. We show that the proposed model fits various event datasets involving single as well as multiple agents better than baseline models. We also illustrate potentially useful insights from our proposed model for an analyst during the discovery process through analysis on a real-world political event dataset.","doi":"10.24963/ijcai.2020/274","url":"https://ijcai.org/proceedings/2020/274","pdf":"https://ijcai.org/proceedings/2020/0274.pdf","markdown":"https://ijcai.org/proceedings/2020/274.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/274","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":274,"pages":{"first":1977,"last":1983},"keywords":["Agent-based and Multi-agent Systems: Multi-agent Learning","Machine Learning: Learning Graphical Models","Data Mining: Mining Spatial, Temporal Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25238/file_25238.m3u8","length_sec":272,"provider":"hls"}],"references":[{"position":1,"raw":"[Aalen et al., 2008] O. 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