We describe a planning algorithm that integrates two ap- proaches to solving Markov decision processes with large state spaces. State abstraction is used to ...

We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to ...

We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to ...

We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid ...

We describe a plnning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to ...

ABSTRACT: AI researchers typically formulated probabilistic planning under uncertainty problems using Markov Decision Processes (MDPs).Value Iteration is an ...

limiting computation to states reachable from the starting state. Main points: First, the authors show how to combine doing factored MDPs with computing only ...

Abstract. We describe a planning algorithm that integrates two ap- proaches to solving Markov decision processes with large state spaces.

This paper introduces a general approach for guiding universal planning based on an existing method for heuristic symbolic search in deterministic domains ...

The most important ideas presented in this paper are the use of a heuristic and reachability analysis to focus the search of SPUDD-like MDP value iteration.