Hierarchical decision-theoretic planning for autonomous robotic surveillance

Surveillance as a first step towards automating the planning of the movement of an autonomous surveillance robot. We extend a previous proposal (N. Massios and F. Voorbraak, 1998), 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.