A Comparison of Analytic and Experimental Goal Regression for Machine Learning

Abstract

Recent research demonstrates the use of goal regression as an analytic technique for learning search heuristics. This paper critically examines this research and identifies essential applicability conditions for the technique. The conditions that operators be invertible and that the domain be closed with respect to the inverse operators severely limit the use of analytic goal regression. In those restricted domains which satisfy the applicability conditions, analytic goal regression only discovers required preconditions for operator application. Discovering pragmatic preconditions is beyond the capability of the technique. An alternative, called experimental goal regression, is defined which approximates the results of analytic goal regression without suffering from these limitations.