Using alternative suboptimality bounds in heuristic search

Richard Valenzano, Shahab Jabbari Arfaee, Roni Stern, Jordan Thayer, Nathan R. Sturtevant

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

16 Scopus citations

Abstract

Most bounded suboptimal algorithms in the search literature have been developed so as to be ε-admissible. This means that the solutions found by these algorithms are guaranteed to be no more than a factor of (1 + ε) greater than optimal. However, this is not the only possible form of suboptimality bounding. For example, another possible suboptimality guarantee is that of additive bounding, which requires that the cost of the solution found is no more than the cost of the optimal solution plus a constant γ. In this work, we consider the problem of developing algorithms so as to satisfy a given, and arbitrary, suboptimality requirement. To do so, we develop a theoretical framework which can be used to construct algorithms for a large class of possible suboptimality paradigms. We then use the framework to develop additively bounded algorithms, and show that in practice these new algorithms effectively trade-off additive solution suboptimality for runtime.

Original languageEnglish
Title of host publicationICAPS 2013 - Proceedings of the 23rd International Conference on Automated Planning and Scheduling
Pages233-241
Number of pages9
StatePublished - 13 Dec 2013
Externally publishedYes
Event23rd International Conference on Automated Planning and Scheduling, ICAPS 2013 - Rome, Italy
Duration: 10 Jun 201314 Jun 2013

Publication series

NameICAPS 2013 - Proceedings of the 23rd International Conference on Automated Planning and Scheduling

Conference

Conference23rd International Conference on Automated Planning and Scheduling, ICAPS 2013
Country/TerritoryItaly
CityRome
Period10/06/1314/06/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems and Management

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