@conference {1502678,
	title = {An extensive study of Dynamic Bayesian Network for patrol allocation against adaptive opportunistic criminals },
	booktitle = {IJCAI{\textquoteright}15 Workshop on Behavioral, Economic and Computational Intelligence for Security (BECIS)},
	year = {2015},
	abstract = {Police patrols are used ubiquitously to deter crimes in
urban areas. A distinctive feature of urban crimes is
that criminals react opportunistically to patrol officers{\textquoteright}
assignments. Different models of adversary behavior have been proposed but their exact form remains
uncertain. Recent work [Zhang et al., 2015] has explored learning the model from real-world criminal
activity data. To that end, criminal behavior and the
interaction with the patrol officers is represented as parameters of a Dynamic Bayesian Network (DBN), enabling application of standard algorithms such as EM
to learn the parameters. More specifically, the EMC2
algorithm is a sequence of modifications to the DBN
representation, that allows for a compact representation resulting in better learning accuracy and increased
speed of learning. In this paper, we perform additional
experiments showing the efficacy of the EMC2
algorithm. Furthermore, we explore different variations of
Markov model. Unlike DBNs, the Markov models do
not have hidden states, which indicate distribution of
criminals, and are therefore easier to learn using standard MLE techniques. We compare all the approaches
by learning from a real data set of criminal activity
obtained from the police department of University of
Southern California (USC) situated in Los Angeles,
USA. We demonstrate a significant better accuracy of
predicting the crime using the EMC2
algorithm compared to other approaches. This work was done in collaboration with the police department of USC.},
	author = {Gholami, Shahrzad and Zhang, Chao and Sinha, Arunesh and Tambe, Milind}
}
