Production systems have successfully made the transition from a trendy research idea to a routinely used programming paradigm. An important cause of this transition has been the several orders of magnitude speedup in program execution achieved in the past few years by the combination of better match algorithms, efficient compilation techniques and faster hardware platforms.

Inductive Logic Programming (ILP) is an emerging research area at the intersection of machine learning, logic programming and software engineering. The first workshop on this topic was held in 1991 in Portugal (Muggleton, 1991). Subsequently, there was a workshop tied to the Future Generation Computer System Conference in Japan in 1992, and a third one in Bled, Slovenia, in April 1993 (Muggleton, 1993). Ideas related to ILP are also appearing in major AI and machine learning conferences and journals. Although European-based and mainly sponsored by ESPRIT, ILP aims at becoming equally represented elsewhere; for example, among researchers in America who are investigating relational learning and first order theory revision (see, for example, the papers in Birnbaum and Collins, 1991) and within the computational learning theory community. This year's IJCAI workshop on ILP is a first step in this direction, and includes recent work with a broader range of perspectives and techniques.

This article summarizes the author's perspective on the discussions that occurred at the Workshop on Explanation and Problem Solving held during the Thirteenth International Joint Conference on Artificial Intelligence*. Motivated by those discussions, the article argues for the promotion of expert system explanation from a secondary task, used mainly for communication, to a primary task that is tightly integrated with the domain problem solving of the expert system.