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Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Rizzini, Mattia, Fawcett, Chris, Vallati, Mauro, Gerevini, Alfonso and Hoos, Holger (2017) Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis. International Journal on Artificial Intelligence Tools, 26 (1). ISSN 0218-2130

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Abstract

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning.

Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Mauro Vallati
Date Deposited: 11 Jan 2017 11:55
Last Modified: 08 Aug 2017 06:33
URI: http://eprints.hud.ac.uk/id/eprint/30881

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