Ariel Felner

Senior Academic

Predicting the performance of IDA* with conditional distributions

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

(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.

Publication language English
Pages 381-386
Publication status Published - 24.12.2008

ASJC Scopus subject areas

Software
Artificial Intelligence
Other files and links
Link to publication in Scopus