Identifying Strategies Using Decision Lists from Trace Information

This paper concerns the issue of learning strategies for problem solvers from trace data. Many works on Explanation Based Learning have proposed methods for speeding up a given problem solver ( or a Prolog program ) by optimizing it on some subspace of problem instances with high probability of occurrences. However, in the current paper, we discuss the issue of identifying a target strategy exactly from trace data. Learning criterion used in this paper is the identification in the limit proposed by Gold. Further, we use the tree pattern language to represent preconditions of operators, and propose a class of strategies, called decision list strategies. One of the interesting features of our learning algorithm is the coupled use of state and operator sequence information of traces. Theoretically, we show that the proposed algorithm identifies some subclass of decision list strategies in the limit with the conjectures updated in polynomial time. Further, an experimental result on N-puzzl