AAAI Publications, Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence

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Towards Detection of Suspicious Behavior from Multiple Observations
Bostjan Kaluza, Gal Kaminka, Milind Tambe

Last modified: 2011-08-24

Abstract


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.

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