Incorporating variance in impact-based search

Authors:
Serdar Kadioglu;Eoin O'Mahony;Philippe Refalo;Meinolf Sellmann
Affiliations:
Brown University, Dept. of Computer Science, Providence, RI;Cornell University, Dept. of Computer Science, Ithaca, NY;IBM, Sophia-Antipolis, France;IBM Watson Research Center, Yorktown Heights, NY
Venue:
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Year:
2011

Citing 4
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Abstract

We present a simple modification to the idea of impact-based search which has proven highly effective for several applications. Impacts measure the average reduction in search space due to propagation after a variable assignment has been committed. Rather than considering the mean reduction only, we consider the idea of incorporating the variance in reduction. Experimental results show that using variance can result in improved search performance.