Safe stochastic planning: Planning to avoid fatal states

Abstract

Markov decision processes (MDPs) are applied as a standard model in Artificial Intelligence planning. MDPs are used to construct optimal or near optimal policies or plans. One area that is often missing from discussions of planning under stochastic environment is how MDPs handle safety constraints expressed as probability of reaching threat states. We introduce a method for finding a value optimal policy satisfying the safety constraint, and report on the validity and effectiveness of our method through a set of experiments. © 2009 Springer Berlin Heidelberg.