Complexity of Conditional Planning under Partial Observability and Infinite Executions

Abstract

The computational properties of many classes of conditional and contingent planning are well known. The main division in the field is between probabilistic planning (typically infinite or unbounded executions, reward rather than goal-based, and focus on expected costs or rewards) and non-probabilistic planning (ignoring probabilities, focus on plans that reach goal states.) In this work, we address the middle ground between these problems: planning with infinite executions and designated goal states. We address {/em worst case} rather than expected costs measures for the problem we consider. We analyze the structure of the plans for two possible goal-based specifications such plans may have to satisfy, maintaining a goal property indefinitely as well as visiting a goal state infinitely often, and establish their complexity under different observability assumptions.