Matthew J. Bays

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— In this paper we present an approach to solving a stochastic multi-target interception problem. In the multi-target interception problem, a team of mobile sensors is tasked with intercepting a set of potential targets to reduce appropriately assigned damage cost. Our principal contribution is to express a stochastic version of the problem with a(More)
— We introduce a novel approach to controlling the motion of a team of agents so that they jointly minimize a cost function utilizing Bayes risk. We use a particle-based approach and approximations that allow us to express the optimization problem as a mixed-integer linear program. We illustrate this approach with an area protection problem in which a team(More)
—In this paper we present a planning approach for the stochastic target interception problem, in which, a team of mobile sensor agents is tasked with intercepting multiple targets. We extend our previous work on stochastic target interception to non-convex domains and propose a cost that addresses minimum time requirement for probabilistically intercepting(More)
—In this paper we propose a framework for optimal coordinated sensor motion using the Bayes risk. For the purpose of illustration, we address an intrusion detection problem, which is cast as a binary hypothesis testing problem. We consider two distinct hypotheses or classes for moving targets. They are classified as threat or safe, depending on the future(More)
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