Cost-Effective Sensing during Plan Execution

Between sensing the world after every action (as in a reactive plan) and not sensing at all (as in an openloop plan), lies a continuum of strategies for sensing during plan execution. If sensing incurs a cost (in time or resources), the most cost-effective strategy is likely to fall somewhere between these two extremes. Yet most work on plan execution assumes one or the other. In this paper, an efficient, anytime planner is described that controls the rate of sensing during plan execution. The sensing interval is determined by the state during plan execution, as well as by the cost of sensing, so that an agent can sense more often when necessary. The planner is based on a generalization of stochastic dynamic programming.