A*pex: Efficient Approximate Multi-Objective Search on Graphs

Han Zhang, Oren Salzman, T. K.Satish Kumar, Ariel Felner, Carlos Hernández Ulloa, Sven Koenig

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

In multi-objective search, edges are annotated with cost vectors consisting of multiple cost components. A path dominates another path with the same start and goal vertices iff the component-wise sum of the cost vectors of the edges of the former path is “less than” the component-wise sum of the cost vectors of the edges of the latter path. The Pareto-optimal solution set is the set of all undominated paths from a given start vertex to a given goal vertex. Its size can be exponential in the size of the graph being searched, which makes multi-objective search time-consuming. In this paper, we therefore study how to find an approximate Pareto-optimal solution set for a user-provided vector of approximation factors. The size of such a solution set can be significantly smaller than the size of the Pareto-optimal solution set, which enables the design of approximate multi-objective search algorithms that are efficient and produce small solution sets. We present such an algorithm in this paper, called A*pex. A*pex builds on PP-A*, a state-of-the-art approximate bi-objective search algorithm (where there are only two cost components) but (1) makes PP-A* more efficient for bi-objective search and (2) generalizes it to multi-objective search for any number of cost components. We first analyze the correctness of A*pex and then experimentally demonstrate its efficiency advantage over existing approximate algorithms for bi- and tri-objective search.

Original languageEnglish
Title of host publicationProceedings of the 32nd International Conference on Automated Planning and Scheduling, ICAPS 2022
EditorsAkshat Kumar, Sylvie Thiebaux, Pradeep Varakantham, William Yeoh
PublisherAssociation for the Advancement of Artificial Intelligence
Pages394-403
Number of pages10
ISBN (Electronic)9781577358749
DOIs
StatePublished - 13 Jun 2022
Externally publishedYes
Event32nd International Conference on Automated Planning and Scheduling, ICAPS 2022 - Virtual, Online, Singapore
Duration: 13 Jun 202224 Jun 2022

Publication series

NameProceedings International Conference on Automated Planning and Scheduling, ICAPS
Volume32
ISSN (Print)2334-0835
ISSN (Electronic)2334-0843

Conference

Conference32nd International Conference on Automated Planning and Scheduling, ICAPS 2022
Country/TerritorySingapore
CityVirtual, Online
Period13/06/2224/06/22

Bibliographical note

Publisher Copyright:
© 2022, Association for the Advancement of Artificial Intelligence.

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