Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods ...
Carlos Guestrin, Milos Hauskrecht, Branislav Kveto...
We address performance issues associated with simulationbased algorithms for optimizing Markov reward processes. Specifically, we are concerned with algorithms that exploit the re...
hsp and hspr are two recent planners that search the state-space using an heuristic function extracted from Strips encodings. hsp does a forward search from the initial state reco...
Planning by forward chaining through the world space has long been dismissed as being "obviously" infeasible. Nevertheless, this approach to planning has many advantages...
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...