TY - JOUR
T1 - Conformant planning via heuristic forward search
T2 - A new approach
AU - Hoffmann, Jörg
AU - Brafman, Ronen I.
N1 - Funding Information:
We would like to thank Piergiorgio Bertoli and Alessandro Cimatti for their help in using MBP and KACMBP, Blai Bonet for his help with GPT, Daniel Bryce for his help with POND, and Dave Smith for his help in understanding and describing related work. We also thank the anonymous reviewers for their comments, which helped to improve the paper. Ronen Brafman is partially supported by the NASA Intelligent Systems Program, and the Paul Ivanier Center for Robotics and Production Management at Ben-Gurion University. Jörg Hoffmann was, at the time of doing the described work, supported by the DFG (Deutsche Forschungsgemeinschaft), project HEU-PLAN II.
PY - 2006/5/1
Y1 - 2006/5/1
N2 - Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic forward-search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF's heuristic function, and of FF's machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.
AB - Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic forward-search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF's heuristic function, and of FF's machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.
KW - Heuristic search planning
KW - Planning under uncertainty
KW - Relaxed plan heuristic
UR - https://www.scopus.com/pages/publications/33645339523
U2 - 10.1016/j.artint.2006.01.003
DO - 10.1016/j.artint.2006.01.003
M3 - Article
AN - SCOPUS:33645339523
SN - 0004-3702
VL - 170
SP - 507
EP - 541
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 6-7
ER -