This paper proposes that managing uncertainty is a control problem, a task for the control component of AI systems that decides what to do next. This view emphasizes the process of planning and executing sequences of actions that simultaneously satisfy domain goals and minimize uncertainty. The paper reviews AI systems that manage uncertainty by control. It is not an exhaustive survey, but rather illustrates issues in managing uncertainty with selected AI programs.

Cognitive emulation is an expert System design strategy which attempts to model System performance on human (expert) thinking. Arguments for and against cognitive emulation are reviewed. A major conclusion is that a significant degree of cognitive emulation is an inherent feature of design, but that an unselective application of the strategy is both unrealistic and undesirable. Pragmatic considerations which limit or facilitate the viability of a cognitive emulation approach are discussed. Particular attention is given to the conflict between cognitive emulation and established knowledge engineering objectives, detailed over 12 typical expert System features. The paper suggests circum-stances in which a strategy of cognitive emulation is useful.

Some of the issues involved in purchasing and using one of the powerful Knowledge Engineering Toolkits are examined. A case study problem is described and several products are reviewed as potential environments for implementing a solution. Experiences of using one System, ART, are described and observations are made regarded the selection, evaluation and use of such packages.

This paper examines the claim that machine induction can alleviate the current knowledge engineering bottleneck in expert system construction. It presents a case study of the rule induction software tool known as Expert-Ease and proposes a set of criteria which might guide the selection of appropriate domains.

Computer induction is discussed in the light of concerns recently expressed by Bloomfield about the use of such algorithms in expert systems construction.

In their paper “Some Comments on Rule Induction” Bratko and Michie address only three of the seven concerns that I put forward in my original article. Significantly, they have chosen to ignore the deeper and more important philosophical issues which I raised, while seeking to deflect my concerns by proclaiming their own privileged position of “being in the know” as experienced rule inductionists. This is a strategy well-known to philosophers and sociologists of science, unfortunately, it tends to stifle debate and avoids the serious consideration of constructive criticism. It is therefore important to respond to the comments made by Bratko and Michie and to re-focus attention on the central themes of my argument.