TY - GEN
T1 - Heuristic planning with SAT
T2 - 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010
AU - Rintanen, Jussi
PY - 2010
Y1 - 2010
N2 - Planning-specific heuristics for SAT have recently been shown to produce planners that match best earlier ones that use other search methods, including the until now dominant heuristic state-space search. The heuristics are simple and natural, and enforce pure depth-first search with backward chaining in the standard conflict-directed clause learning (CDCL) framework. In this work we consider alternatives to pure depth-first search, and show that carefully chosen randomized search order, which is not strictly depth-first, allows to leverage the intrinsic strengths of CDCL better, and will lead to a planner that clearly outperforms existing planners.
AB - Planning-specific heuristics for SAT have recently been shown to produce planners that match best earlier ones that use other search methods, including the until now dominant heuristic state-space search. The heuristics are simple and natural, and enforce pure depth-first search with backward chaining in the standard conflict-directed clause learning (CDCL) framework. In this work we consider alternatives to pure depth-first search, and show that carefully chosen randomized search order, which is not strictly depth-first, allows to leverage the intrinsic strengths of CDCL better, and will lead to a planner that clearly outperforms existing planners.
UR - http://www.scopus.com/inward/record.url?scp=78650775470&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17432-2_42
DO - 10.1007/978-3-642-17432-2_42
M3 - Conference contribution
SN - 3642174310
SN - 9783642174315
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 415
EP - 424
BT - AI 2010
Y2 - 7 December 2010 through 10 December 2010
ER -