A-A*pex: Efficient Anytime Approximate Multi-Objective Search

Han Zhang, Oren Salzman, Ariel Felner, Carlos Hernández Ulloa, Sven Koenig

Research output: Contribution to journalConference articlepeer-review


In the multi-objective search problem, a typical task is to compute the Pareto frontier, i.e., the set of all undominated solutions. However, computing the entire Pareto frontier can be very time-consuming, and in practice, we often have limited deliberation time. Therefore, this paper focuses on solving the multi-objective search problem with anytime algorithms, which compute an initial approximate frontier quickly and then work to find more solutions until eventually finding the entire Pareto frontier. Existing work has investigated such anytime algorithms for problem instances with only two objectives. In this paper, we propose Anytime A*pex (AA*pex), which works with any number of objectives. In each iteration of A-A*pex, it runs A*pex, a state-of-the-art approximate multi-objective search algorithm, to compute more solutions. From one iteration to the next, A-A*pex can either reuse its previous search effort or restart from scratch. Our experimental results show that an A-A*pex variant that mixes reusing its search effort and restarting from scratch yields the best runtime performance. We also show that A-A*pex often computes solutions that collectively approximate the Pareto frontier much better than the solutions found by state-of-theart multi-objective search algorithms for short deliberation times.

Original languageEnglish
Pages (from-to)179-187
Number of pages9
JournalThe International Symposium on Combinatorial Search
Issue number1
StatePublished - 2024
Externally publishedYes
Event17th International Symposium on Combinatorial Search, SoCS 2024 - Kananaskis, Canada
Duration: 6 Jun 20248 Jun 2024

Bibliographical note

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© 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.


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