Portfolio Methods for Optimal Planning: an Empirical Analysis

Rizzini, Mattia, Fawcett, Chris, Vallati, Mauro, Gerevini, Alfonso Emilio and Hoos, Holger (2015) Portfolio Methods for Optimal Planning: an Empirical Analysis. In: Proceedings of the 27th IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer Society, pp. 494-501.

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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 (domain- independent) 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 experimental 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:	Book Chapter
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools:	School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
Related URLs:	Author Conference
Depositing User:	Mauro Vallati
Date Deposited:	15 Sep 2015 12:56
Last Modified:	30 Mar 2018 09:01
URI:	http://eprints.hud.ac.uk/id/eprint/25692

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