Partial-Expansion A* with Selective Node Generation

Ariel Felner, Meir Goldenberg, Guni Sharon, Roni Stern, Tal Beja, Nathan Sturtevant, Jonathan Schaeffer, Robert C. Holte

Research output: Contribution to conferencePaperpeer-review


A* is often described as being 'optimal', in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* (PEA*) (Yoshizumi, Miura, and Ishida 2000) addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. n is re-inserted into the OPEN list, but with the f-cost of the next best child. This paper introduces an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* generates only the children with the same f-cost as the parent. EPEA* is generalized to its iterative-deepening variant, EPE-IDA*. For some domains, these algorithms yield substantial performance improvements. State-of-the-art results were obtained for the pancake puzzle and for some multi-agent pathfinding instances. Drawbacks of EPEA* are also discussed.

Original languageEnglish
Number of pages7
StatePublished - 2012
Externally publishedYes
Event26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada
Duration: 22 Jul 201226 Jul 2012


Conference26th AAAI Conference on Artificial Intelligence, AAAI 2012

Bibliographical note

Publisher Copyright:
Copyright © 2012, Association for the Advancement of Artificial Intelligence ( All rights reserved.

ASJC Scopus subject areas

  • Artificial Intelligence


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