9310819 Mooney A general goal of machine learning is automating the construction of efficient knowledge-based systems. Current methods tend to focus on either the acquisition of the basic domain knowledge (inductive learning) or the acquisition of search-control knowledge (speedup learning). However, these two tasks are not always cleanly separated. This research investigates the general framework of learning search-control heuristics for logic programs, which can improve both accuracy and efficiency. Logic programming provides a very general and well-understood representational and computational platform on which to build. Recent methods in inductive logic programming can be used to automatically learn search-control heuristics from a set of training problems. Efficiency may be improved by learning control rules that eliminate backtracking during the search for a proof. Accuracy may be improved by learning control rules that eliminate search paths that produce incorrect results.
