A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Belief state approaches to signaling alarms in surveillance systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Belief selection in point-based planning algorithms for POMDPs
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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The potentially catastrophic impact of a bioterrorist attack makes developing effective detection methods essential for public health. In the case of anthrax attack, a delay of hours in making a right decision can lead to hundreds of lives lost. Current detection methods trade off reliability of alarms for early detection of outbreaks. The performance of these methods can be improved by modem disease-specific modeling techniques which take into account the potential costs and effects of an attack to provide optimal warnings. We study this optimization problem in the reinforcement learning framework. The key contribution of this paper is to apply Partially Observable Markov Decision Processes (POMDPs) on outbreak detection mechanism for improving alarm function in anthrax outbreak detection. Our approach relies on estimating the future benefit of true alarms and the costs of false alarms and using these quantities to identify an optimal decision. We present empirical evidence illustrating that the performance of detection methods with respect to sensitivity and timeliness is improved significantly by utilizing POMDPs.