Max Is More than Min: Solving Maximization Problems with Heuristic Search

Ron Zvi Stern, Rami Puzis, Ariel Felner, Scott Kiesel, Wheeler Ruml

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

11 Scopus citations

Abstract

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding the longest simple path in a graph, maximal coverage, and various constraint optimization problems. We examine several popular search algorithms for MIN problems — optimal, suboptimal, and bounded suboptimal — and discover the curious ways in which they misbehave on MAX problems. We propose modifications that preserve the original intentions behind the algorithms but allow them to solve MAX problems, and compare them theoretically and empirically. Interesting results include the failure of bidirectional search and a discovered close relationships between Dijkstra’s algorithm, weighted A*, and depth-first search. This work demonstrates that MAX problems demand their own heuristic search algorithms, which are worthy objects of study in their own right.

Original languageEnglish
Title of host publicationProceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
EditorsStefan Edelkamp, Roman Bartak
PublisherAAAI press
Pages148-156
Number of pages9
ISBN (Electronic)9781577356769
StatePublished - 1 Jan 2014
Externally publishedYes
Event7th Annual Symposium on Combinatorial Search, SoCS 2014 - Prague, Czech Republic
Duration: 15 Aug 201417 Aug 2014

Publication series

NameProceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
Volume2014-January

Conference

Conference7th Annual Symposium on Combinatorial Search, SoCS 2014
Country/TerritoryCzech Republic
CityPrague
Period15/08/1417/08/14

ASJC Scopus subject areas

  • Computer Networks and Communications

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