LiU Electronic Press
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Author:
Haslum, Patrik (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Bonet, Blai (Departamento de Computación Universidad Simón Bolívar)
Geffner, Hector (Departamento de Tecnologia Universitat Pompeu Fabra)
Title:
New Admissible Heuristics for Domain-Independent Planning
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the 20th national ´Conference on Artificial Intelligence (AAAI)
Publisher: AAAI Press
Pages:
1163-
Year of publ.:
2005
URI:
urn:nbn:se:liu:diva-31789
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-31789
ISBN:
1-57735-236-X
Local ID:
17613
Subject category:
Computer Science
SVEP category:
Computer science
Abstract(en) :

Admissible heuristics are critical for effective domain-independent planning when optimal solutions must be guaranteed. Two useful heuristics are the hm heuristics, which generalize the reachability heuristic underlying the planning graph, and pattern database heuristics. These heuristics, however, have serious limitations: reachability heuristics capture only the cost of critical paths in a relaxed problem, ignoring the cost of other relevant paths, while PDB heuristics, additive or not, cannot accommodate too many variables in patterns, and methods for automatically selecting patterns that produce good estimates are not known.

We introduce two refinements of these heuristics: First, the additive hm heuristic which yields an admissible sum of hm heuristics using a partitioning of the set of actions. Second, the constrained PDB heuristic which uses constraints from the original problem to strengthen the lower bounds obtained from abstractions.

The new heuristics depend on the way the actions or problem variables are partitioned. We advance methods for automatically deriving additive hm and PDB heuristics from STRIPS encodings. Evaluation shows improvement over existing heuristics in several domains, although, not surprisingly, no heuristic dominates all the others over all domains.

Available from:
2009-10-09
Created:
2009-10-09
Last updated:
2011-02-27
Statistics:
9 hits