Hierarchical Decision-Theoretic Robotic Surveillance
Nikos Massios, Frans Voorbraak

In this paper, we discuss a decision-theoretic strategy for
surveillance as a first step towards automating the planning of the
movement of an autonomous surveillance robot. We extend a previous
proposal by including some heuristics based on an abstract
representation of the environment. We show, using a concrete example,
how these heuristics allow computationally feasible, finite look-ahead
versions of the decision-theoretic strategy to escape local minima,
and to better approximate globally optimal solutions.