Towards detection of suspicious behavior from multiple observations

Boštjan Kaluža, Gal Kaminka, Milind Tambe

Research output: Contribution to journalArticlepeer-review


This paper addresses the problem of detecting suspicious behavior from a collection of individuals events, where no single event is enough to decide whether his/her behavior is suspicious, but the combination of multiple events enables reasoning. We establish a Bayesian framework for evaluating multiple events and show that the current approaches lack modeling behavior history included in the estimation whether a trace of events is generated by a suspicious agent. We propose a heuristic for evaluating events according to the behavior of the agent in the past. The proposed approach, tested on an airport domain, outperforms the current approaches. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.
Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalAAAI Workshop - Technical Report
StatePublished - 31 Oct 2011


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