We present a technique for computing approximately optimal solutions to stochastic resource allocation problems modeled as Markov decision processes (MDPs). We exploit two key pro...
Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, L...
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Abstract. We compare two prominent decision procedures for propositional logic: Ordered Binary Decision Diagrams (obdds) and the DavisPutnam procedure. Experimental results indicat...
Probabilistic AI planning methods that minimize expected execution cost have a neutral attitude towards risk. We demonstrate how one can transform planning problems for risk-sensi...
This paper present a class of investment problem, in which many items could be chosen in a group decision environment. Usually there is a decision table from the board of director...