Optimizing search with positive information feedback

Abstract

A search model is formulated in which positive information may be obtained, through the detection of trails, as to the target's earlier whereabouts. The corresponding Bayesian update formulas for target location probabilities are derived. The model does not appear to be amenable to rigorous optimization. A moving-horizon rule, and a heuristic simplification thereof, are, however, derived. In two numerical examples it is demonstrated that actively designing for detecting trail information, through use of these moving-horizon rules, has substantial potential advantage over using, for example, myopic rules even if the positive information is adaptively incorporated into location probabilities before applying the latter rules in each time period.