TY - GEN
T1 - Regression for classical and nondeterministic planning
AU - Rintanen, Jussi
N1 - Publisher Copyright:
© 2008 The authors and IOS Press. All rights reserved.
PY - 2008/6
Y1 - 2008/6
N2 - Many forms of reasoning about actions and planning can be reduced to regression, the computation of the weakest precondition a state has to satisfy to guarantee the satisfaction of another condition in the successor state. In this work we formalize a general syntactic regression operation for ground PDDL operators, show its correctness, and define a composition operation based on regression. As applications we present a very simple yet powerful algorithm for computing invariants, as well as a generalization of the hn heuristic of Haslum and Geffner to PDDL.
AB - Many forms of reasoning about actions and planning can be reduced to regression, the computation of the weakest precondition a state has to satisfy to guarantee the satisfaction of another condition in the successor state. In this work we formalize a general syntactic regression operation for ground PDDL operators, show its correctness, and define a composition operation based on regression. As applications we present a very simple yet powerful algorithm for computing invariants, as well as a generalization of the hn heuristic of Haslum and Geffner to PDDL.
UR - http://www.scopus.com/inward/record.url?scp=84923212653&partnerID=8YFLogxK
U2 - 10.3233/978-1-58603-891-5-568
DO - 10.3233/978-1-58603-891-5-568
M3 - Conference contribution
SN - 978158603891
T3 - Frontiers in Artificial Intelligence and Applications
SP - 568
EP - 572
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press BV
T2 - 18th European Conference on Artificial Intelligence, ECAI 2008
Y2 - 21 July 2008 through 25 July 2008
ER -