This symposium was very successful and was perhaps the most unusual of the spring symposia this year. It brought together for the first time distinguished researchers from many diverse disciplines to discuss and share results on a particular topic of mutual interest. The disciplines included machine learning, computational learning theory, computer vision, pattern recognition, perceptual psychology, statistics, information theory, theoretical computer science, and molecular biology, with the involvement of the latter group having lead to a joint session with the AI and Molecular Biology symposium.

After explicating the need for a large commonsense knowledge base spanning human consensus knowledge, we report on many of the lessons learned over the first five years of attempting its construction. We have come a long way in terms of methodology, representation language, techniques for efficient inferencing, the ontology of the knowledge base, and the environment and infrastructure in which the knowledge base is being built. We describe the evolution of Cyc and its current state and close with a look at our plans and expectations for the coming five years, including an argument for how and why the project might conclude at the end of this time.

Automated knowledge-acquisition systems have focused on embedding a cognitive model of a key knowledge worker in their software that allows the system to acquire a knowledge base by interviewing domain experts just as the knowledge worker would. Two sets of research questions arise: (1) What theories, strategies, and approaches will let the modeling process be facilitated; accelerated; and, possibly, automated? If automated knowledge-acquisition systems reduce the bottleneck associated with acquiring knowledge bases, how can the bottleneck of building the automated knowledge-acquisition system itself be broken? How can an automated system critique and influence such biases in a positive fashion, what common patterns exist across applications, and can models of influencing behavior be described and standardized?

The two-volume set entitled "Knowledge-Based Systems (Volume 1, Knowledge Acquisition for Knowledge-Based Systems, 355 pp., and Volume 2, "Knowledge Acquisition Tools for Expert Systems, 343 pp., Academic Press, San Diego, California, 1988), edited by B. R. Gaines and J. H. Boose, is an excellent collection of papers useful to both commercial practitioners of knowledge-based-systems development and research-oriented scientists at specialized centers or academic institutions.

Commentators on AI converge on two goals they believe define the field: (1) to better understand the mind by specifying computational models and (2) to construct computer systems that perform actions traditionally regarded as mental. We should recognize that AI has a third, hidden, more basic aim; that the first two goals are special cases of the third; and that the actual technical substance of AI concerns only this more basic aim. This third aim is to establish new computation-based representational media, media in which human intellect can come to express itself with different clarity and force. This article articulates this proposal by showing how the intellectual activity we label AI can be likened in revealing ways to each of five familiar technologies.

The specific motivations underlying this research include the following propositions: (1) Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. Furthermore, the model can help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system that draws on structured representations of the clinical literature to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer-management options based on the 24 clinical studies currently encoded in its knowledge base.

Within the academic and professional auditing communities, there has been growing concern about how to accurately assess the various risks associated with performing an audit. These risks are difficult to conceptualize in terms of numeric estimates.

VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking

This article examines the direction in which knowledge bases are constructed for diagnosis and decision making. When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses. Therefore, we argue that a knowledge base be constructed following the expert's natural reasoning direction, and then reverse the direction for use. This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty.

This article discusses several effective techniques for expert system knowledge acquisition based on the techniques that were successfully used to develop the Central Office Maintenance Printout Analysis and Suggestion System (COMPASS). Knowledge acquisition is not a science, and expert system developers and experts must tailor their methodologies to fit their situation and the people involved. Developers of future expert systems should find a description of proven knowledge-acquisition techniques and an account of the experience of the COMPASS project in applying these techniques to be useful in developing their own knowledge-acquisition procedures.