The representation of parts of legislation in logic, successively implemented in the language of logic programming and managed by Prolog interpreters, has by now existed for more than ten years. The first and most well-known projects were those by the Logic Programming Group of Imperial College of London which, in 1985, formalized the British Nationality Act (Sergot et al., 1986; Sergot, 1990). Other projects followed, for the most part European, including the Italian project, Esplex, developed in Florence (Biagioli et al., 1987), the Dutch project, Prolex, (Walker et al., 1990), the German project born of the collaboration between IBM and the University of Tubingen (Alschwee, Grundrnann, 1986), and the Japanese project, Les-2 (Yoshino, 1986).

I thank the commentators for their time, and generally positive remarks on the promise of the task-specific approach, in particular the generic task (GT) proposal.

Johnson and Zualkernan would like a methodology for mapping domain knowledge onto one or more generic tasks so as to solve problems efficiently. This stage of problem and domain analysis in which the kind of reasoning that goes on needs to be analysed in a vocabulary of generic tasks is very important, and in our laboratory we identify this as the epistemic analysis stage. For specific classes of problems we have developed guidelines on how to perform this mapping. For example, Bylander and Smith (Bylander and Smith, 1985) describe a set of criteria and guidelines for mapping medical knowledge into CSRL-like structures for diagnostic reasoning. Similarly Brown (1984) describes criteria for mapping design knowledge into DSPL-like structures.

This paper provides a survey of the state of the art in plausible reasoning, that is exception tolerant reasoning under incomplete information. Three requirements are necessary for a formalism in order to cope with this problem: (i) making a clear distinction between factual information and generic knowledge; (ii) having a correct representation of partial ignorance; (iii) providing a nonmonotonic inference mechanism. Classical logic fails on requirements (i) and (iii), whilst the Bayesian approach does not fulfil (ii) in an unbiased way. In this perspective, various uncertainty modelling frameworks are reviewed: MYCIN-like fully compositional calculi, belief functions, upper and lower probability systems, and possibility theory. Possibility theory enables classical logic to be extended to layered sets of formulae, where layers express certainty levels. Finally, it is explained how generic knowledge can be expressed by constraints on possibility measures, and how possibilistic inferences can encode nonmonotonic reasoning in agreement with the Lehmann et al. postulates.

The first conference on Uncertainty in Artificial Intelligence was held in 1985 by a group of people who felt that their views on the use of probability theory were not receiving a fair hearing from the rest of the Al community. At the time, mainstream opinion held that computational complexity of, and the amount of data required by, probabilistic methods made them inappropriate for realistic applications. As a result, those who claimed that probability theory was an adequate, if not the only adequate, method of handling uncertainty received a somewhat frosty reception.

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.

Computational reflection is the activity performed by a computational System when reasoning about (and by that possibly affecting) itself. This paper presents an introduction to computational reflection (thereafter called reflection). A definition of reflection is presented, its utility for knowledge engineering is discussed and architectures of languages that support it are studied. Examples of such procedural, logic-based, rule-based and object-oriented languages are presented. The paper elaborates on the design of these languages and the reflective functionality that results, elucidating concepts such as procedural reflection, declarative reflection, theory relativity of reflection, etc. The paper concludes with an assessment of outstanding problems and future developments in the area.