Computer Science Department
School of Computer Science, Carnegie Mellon University


Statistical Selection Among Problem-Solving Methods

Eugene Fink

January 1997

Keywords: Learning, problem solving, statistical analysis

The choice of an appropriate problem-solving method, from available methods, is a crucial skill for human experts in many areas. We describe a technique for automatic selection among methods, based on a statistical analysis of their past performances.

We formalize the statistical problem involved in selecting an efficient problem-solving method, derive a solution to this problem, and describe a selection algorithm. The algorithm not only chooses among available methods, but also decides when to abandon the chosen method, if it proves to take too much time. We extend our basic statistical technique to account for problem sizes and for similarity between problems.

We give empirical results of the use of this technique to select among search engines in the Prodigy system. We also test the selection technique on artificially generated performance data, using several different probability distributions.

39 pages

Return to: SCS Technical Report Collection
School of Computer Science homepage

This page maintained by