Predicting the performance of IDA* with conditional distributions

Uzi Zahavi, Ariel Felner, Neil Burch, Robert C. Holte

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

7 Scopus citations

Abstract

(Korf, Reid, and Edelkamp 2001) introduced a formula to predict the number of nodes IDAM× will expand given the static distribution of heuristic values. Their formula proved to be very accurate but it is only accurate under the following limitations: (1) the heuristic must be consistent; (2) the prediction is for a large random sample of start states (or for large thresholds). In this paper we generalize the static distribution to a conditional distribution of heuristic values. We then propose a new formula for predicting the performance of IDA× that works well for inconsistent heuristics (Zahavi et al. 2007) and for any set of start states, not just a random sample. We also show how the formula can be enhanced to work well for single start states. Experimental results demonstrate the accuracy of our method in all these situations.

Original languageEnglish
Title of host publicationAAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference
Pages381-386
Number of pages6
StatePublished - 24 Dec 2008
Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
Duration: 13 Jul 200817 Jul 2008

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Conference

Conference23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
Country/TerritoryUnited States
CityChicago, IL
Period13/07/0817/07/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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