Stochastic Deliberation Scheduling using GSMDPs

We propose a new decision-theoretic approach for solving execution-time deliberation scheduling problems using recent advances in Generalized Semi-Markov Decision Processes (GSMDPs). In particular, we use GSMDPs to more accurately model domains in which planning and execution occur concurrently, planimprovement actions have uncertain effects and duration, and events (such as threats) occur asynchronously and stochastically. We demonstrate a significant improvement in expressibility over previous discrete-time approximate models in which mission phase duration was fixed, failure events were synchronized with phase transitions, and planning time was discretized into constant-size planning quanta.