RAO: An algorithm for chance-constrained POMDP's

Autonomous agents operating in partially observable stochastic environments often face the problem of optimizing expected performance while bounding the risk of violating safety constraints. Such problems can be modeled as chance-constrained POMDP's (CCPOMDP's). Our first contribution is a systematic derivation of execution risk in POMDP domains, which improves upon how chance constraints are handled in the constrained POMDP literature. Second, we present RAO, a heuristic forward search algorithm producing optimal, deterministic, finite-horizon policies for CCPOMDP's. In addition to the utility heuristic, RAO leverages an admissible execution risk heuristic to quickly detect and prune overly-risky policy branches. Third, we demonstrate the usefulness of RAO in two challenging domains of practical interest: power supply restoration and autonomous science agents.