A variety of proposals for preferred methodological approaches has been advanced in the recent artificial intelligence (AI) literature. Rather than advocating a particular approach, this article attempts to explain the apparent confusion of efforts in the field in terms of differences among underlying methodological perspectives held by practicing researchers. The article presents a review of such perspectives discussed in the existing literature and then considers a descriptive and relatively specific typology of these differing research perspectives. It is argued that researchers should make their methodological orientations explicit when communicating research results, to increase both the quality of research reports and their comprehensibility for other participants in the field. For a reader of the AI literature, an understanding of the various methodological perspectives will be of immediate benefit, giving a framework for understanding and evaluating research reports. In addition, explicit attention to methodological commitments might be a step towards providing a coherent intellectual structure that can be more easily assimilated by newcomers to the field.

Getting started on a new knowledge engineering project is a difficult and challenging task, even for those who have done it before. For those who haven't, the task can often prove impossible. One reason is that the requirements-oriented methods and intuitions learned in the development of other types of software do not carry over well to the knowledge engineering task. Another reason is that methodologies for developing expert systems by extracting, representing, and manipulating an expert's knowledge have been slow in coming. At Tektronix, we have been using step-by-step approach to prototyping expert systems for over two years now. The primary features of this approach are that it gives software engineers who do not know knowledge engineering an easy place to start, and that it proceeds in a step-by-step fashion from initiation to implementation without inducing conceptual bottlenecks into the development process. This methodology has helped us collect the knowledge necessary to implement several prototype knowledge-based systems, including a troubleshooting assistant for the Tektronix FG-502 function generator and an operator's assistant for a wave solder machine.

The choice of implication as a representation for empirical associations and for deduction as a model of inference requires a mechanism extraneous to deduction to manage uncertainty associated with inference. Consequently, the interpretation of representations of uncertainty is unclear. Representativeness, or degree of fit, is proposed as an interpretation of degree of belief for classification tasks. The calculation of representativeness depends on the nature of the associations between evidence and conclusions. Patterns of associations are characterized as endorsements of conclusions. We discuss an expert system that uses endorsements to control the search for the most representative conclusion, given evidence.

The fact was there were a lot of failures. There I have been assigned the role of survivalist. First I want to were overruns and systems delivered past schedule. This ask, "Has AI paid its way?"... Or to put it another way, is certainly not unique to Naval Electronic System Command. "Have we earned our keep?" I have three answers to that: The most would be systems being acquired for the Yes, yes, and yes.

The enterprise of developing knowledge-based systems is currently witnessing great growth in popularity. The central unity of many such programs is that they interpret knowledge that is explicitly encoded as rules. While rule-based programming comes with certain clear pay-offs, further fundamental advances in research are needed to extend the scope of tasks that can be adequately represented in this fashion. This article is a statement of personal perspective by a researcher interested in fundamental issues in the symbolic representation and organization ok knowledge.

Tsukuba Expo '85 is huge, interesting, and fun. The Japanese pavilions are plush and well -organized and contain some impressive artificial intelligence demonstrations. The U.S. pavilion is an embarrassment.

Over the past few years, the notion of a "prototype" (e.g., TYPICAL-ELEPHANT) seems to have caught on securely in knowledge representation research. Along with a way to specify default properties for instances of a description, proto-representations allow overriding, or "canceling" of properties that don't apply in particular cases. This supposedly makes representing exceptions ( three-legged elephants and the like ) easy; but, alas, it makes one crucial type of representation impossible-that of composite descriptions whose meanings are functions of the structure and interrelation of their parts. This article explores this and other ramifications of the emphasis on default properties and "typical" objects.

And even if verification to be accommodated within the SPIV paradigm. But until were possible it would not contribute very much to the such time as we find these learning algorithms (and I development of production software. Hence "verifiability don't think that many would argue that such algorithms must not be allowed to overshadow reliability. Scientists will be available in the foreseeable future) we must face should not confuse mathematical models with reality." the prospect of systems that will need to be modified, in AI is perhaps not so special, it is rather an extreme nontrivial ways, throughout their useful lives. Thus incremental and thus certain of its characteristics are more obvious development will be a constant feature of such than in conventional software applications. Thus the SPIV software and if it is not fully automatic then it will be part methodology may be inappropriate for an even larger class of the human maintenance of the system. I am, of course, of problems than those of AI. not suggesting that the products of say architectural design I have raised all these points not to try to deny the (i.e., buildings) will need a learning capability. Nevertheless, worth of Mostow's ideas and issues concerning the design a final fixed design, that remains "optimal" in a process, but to make the case that such endeavors should dynamically changing world, is a rare event.The similarity also be pursued within a fundamentally incremental and between AI system development and the design of more evolutionary framework for design. The potential of the concrete objects is still present, but it is, in some respects, RUDE paradigm is deserving of more attention than it is rather tenuous I admit.

At the start of a project looking into the development of an expert system, the knowledge engineering project team must investigate one or several possible expert system domains. To evaluate the potential of possible application domains, it has proved very useful to have a set of desired attributes for good expert domain. The attribute set was developed as part of a major expert system development project at GTE Laboratories. It was used recurrently (and modified and expanded continually) throughout an extensive application domain evaluation and selection process.

Expert system projects are often based on collaboration with single domain expert. This leads to difficulties in judging the suitability of the chosen task and in acquiring the detailed knowledge required to carry out the task. This anecdotal article considers some of the advantages of using a diverse collection of domain experts.