Heuristic Search Strategies in MEA-based Problem Solver and Their Case-based Knowledge Acquisition

Abstract

This paper presents two kinds of search strategies for a means-ends analysis and their knowledge acquisition from problem solving cases. One strategy rejects an applicable operator on the search tree if an action, called the forestalling operator, can be applied to the current state space. The other strategy makes the problem solver clear all of the stacked goals except the last one, where the problem solver regards the pattern of the current state space on the search tree as one solved before. The knowledge for these strategies, as well as the control rules for the original means-ends analysis, is acquired by detecting the case that the problem solver has made a mistake.