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We propose and study an iterative Forward And Backward (FAB) algorithm applicable to the problem of computing optimal search plans when the target's motion is modeled by a discrete space and time Markov chain. This paper generalizes the class of objective functions to which the method applies.

The problem of searching for randomly moving targets such as children and submarines is known to be fundamentally difficult, but finding efficient methods for generating optimal or near optimal solutions is nonetheless an important practical problem. This paper investigates the efficiency of Branch and Bound methods, with emphasis on the tradeoff between the accuracy of the bound employed and the time required to compute it. A variety of bounds are investigated, some of which are new. In most cases the best bounds turn out to be imprecise, but very easy to compute.

No abstract