Knowledge-based systems, problem solving competence and learnability

Abstract

Improved methods for development and maintenance of real world knowledge-based systems are strongly needed. It is a challenge for artificial intelligence research to develop methods that will make the building of such systems feasible. The work described in this paper is a contribution to that research. The problem addressed in this paper is that of developing a framework/system "X" which integrates problem solving with learning from experience within an extensive model of different knowledge types. "X" has a reasoning strategy which first attempts case-based reasoning, then rule-based reasoning, and, finally, model-based reasoning. It learns from each problem solving session by updating its collection of cases, irrespective of which reasoning method that succeeded in solving the problem. © 2011 Springer-Verlag.