TY - CONF
T1 - A Multi-Path Compilation Approach to Contingent Planning
AU - Brafman, Ronen I.
AU - Shani, Guy
N1 - Funding Information: all eventualities. Workarounds exist, such as focusing on the first time each action is performed, as suggested by Pala-cios and Geffner (2009), which is well suited for translation based methods. Nevertheless, building a contingent planner that handles non-deterministic domains well remains an important challenge. Acknowledgement: Ronen Brafman is partially supported by ISF grant 8254320, the Paul Ivanier Center for Robotics Research and Production Management, and the Lynn and William Frankel Center for Computer Science. Publisher Copyright: Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2012/1/1
Y1 - 2012/1/1
N2 - We describe a new sound and complete method for compiling contingent planning problems with sensing actions into classical planning. Our method encodes conditional plans within a linear, classical plan. This allows our planner, MPSR, to reason about multiple future outcomes of sensing actions, and makes it less susceptible to dead-ends. MPRS, however, generates very large classical planning problems. To overcome this, we use an incomplete variant of the method, based on state sampling, within an online replanner. On most current domains, MPSR finds plans faster, although its plans are often longer. But on a new challenging variant of Wumpus with dead-ends, it finds smaller plans, faster, and scales better.
AB - We describe a new sound and complete method for compiling contingent planning problems with sensing actions into classical planning. Our method encodes conditional plans within a linear, classical plan. This allows our planner, MPSR, to reason about multiple future outcomes of sensing actions, and makes it less susceptible to dead-ends. MPRS, however, generates very large classical planning problems. To overcome this, we use an incomplete variant of the method, based on state sampling, within an online replanner. On most current domains, MPSR finds plans faster, although its plans are often longer. But on a new challenging variant of Wumpus with dead-ends, it finds smaller plans, faster, and scales better.
UR - http://www.scopus.com/inward/record.url?scp=85030627981&partnerID=8YFLogxK
M3 - Paper
SP - 1868
EP - 1874
T2 - 26th AAAI Conference on Artificial Intelligence, AAAI 2012
Y2 - 22 July 2012 through 26 July 2012
ER -