TY - GEN
T1 - Conformant planning via heuristic forward search
T2 - Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004
AU - Brafman, Ronen I.
AU - Hoffmann, Jörg
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 - 2004/12/1
Y1 - 2004/12/1
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 deciding solvability of CNFs that capture the action sequence's semantics. Based on this approach, we extend the classical heuristic planning system FF to the conformant setting. The key to this extension is the introduction of approximative CNF reasoning in FF's heuristic function. Our experimental evaluation shows Conformant-FF 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 deciding solvability of CNFs that capture the action sequence's semantics. Based on this approach, we extend the classical heuristic planning system FF to the conformant setting. The key to this extension is the introduction of approximative CNF reasoning in FF's heuristic function. Our experimental evaluation shows Conformant-FF to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.
UR - http://www.scopus.com/inward/record.url?scp=13444256696&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:13444256696
SN - 1577352009
SN - 9781577352006
T3 - Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004
SP - 355
EP - 364
BT - Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004
A2 - Zilberstein, S.
A2 - Koehler, J.
A2 - Koenig, S.
Y2 - 3 June 2004 through 7 June 2004
ER -