Stochastic Deliberation Scheduling using GSMDPs

We propose a new decision-theoretic approach for solv- ing 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, plan- improvement actions have uncertain effects and dura- tion, and events (such as threats) occur asynchronously and stochastically. We demonstrate a significant im- provement 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.