mGPT: A Probabilistic Planner Based on Heuristic Search

Abstract

We describe the version of the GPT planner used in the probabilistic track of the 4th International Planning Competition (IPC-4). This version, called mGPT, solves Markov Decision Processes specified in the PPDDL language by extracting and using different classes of lower bounds along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations where the alternative probabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state and the greedy policy.

References in corpus (1)

• The FF Planning System: Fast Plan Generation Through Heuristic Search

Cited by in corpus (6)

• Decision-Theoretic Planning with non-Markovian Rewards
• A Theory of Goal-Oriented MDPs with Dead Ends
• Practical Linear Value-approximation Techniques for First-order MDPs
• DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning
• Engineering a Conformant Probabilistic Planner
• Generalizing the Role of Determinization in Probabilistic Planning