Learn More
Progress has been made recently in developing techniques to automatically generate effective heuristics. These techniques typically aim to reduce the size of the search tree, usually by combining more primitive heuristics. However, simply reducing search tree size is not enough to guarantee that problems will be solved more quickly. We describe a new(More)
This paper describes a system that automatically transforms a PDDL encoding, calls a planner to solve the transformed representation, and translates the solution back into the original representation. The approach involves counting objects that are indistinguishable, rather than treating them as individuals, which eliminates some unnecessary combinatorial(More)
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(More)
In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A* search. Our methods are able to optimize various objective functions while selecting a subset from a pool of up to thousands of heuristics. Specifically , our methods minimize approximations of A*'s search tree size, and approximations of A*'s running time.(More)