mGPT: a probabilistic planner based on heuristic search

Abstract

We describe the version of the GPT planner to be used in the planning competition. This version, called mGPT, solves mdps specified in the ppddl/nlanguage by extracting and using different classes of lower bounds, along with various heuristic-search /nalgorithms. The lower bounds are extracted from deterministic relaxations of the mdp where alternative/nprobabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms, on the other hand, use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state with the greedy policy.