In order for next-generation expert systems to demonstrate the performance, robustness, flexibility, and learning ability of human experts, they will have to be based on cognitive models of expert human reasoning and learning. We call such next-generation systems cognitive expert systems. Research at the Artificial Intelligence Laboratory at the University of Connecticut is directed toward understanding the principles underlying cognitive expert systems and developing computer programs embodying those principles. The Causal Model Acquisition System (CMACS) learns causal models of physical mechanisms by understanding real-world natural language explanations of those mechanisms.

Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.

In order for next-generation expert systems to demonstrate the performance, robustness, flexibility, and learning ability of human experts, they will have to be based on cognitive models of expert human reasoning and learning. We call such next-generation systems cognitive expert systems. Research at the Artificial Intelligence Laboratory at the University of Connecticut is directed toward understanding the principles underlying cognitive expert systems and developing computer programs embodying those principles. The Causal Model Acquisition System (CMACS) learns causal models of physical mechanisms by understanding real-world natural language explanations of those mechanisms. The going Concern Expert ( GCX) uses business and environmental knowledge to assess whether a company will remain in business for at least the following year. The Business Information System (BIS) acquires business and environmental knowledge from in-depth reading of real-world news stories. These systems are based on theories of expert human reasoning and learning, and thus represent steps toward next-generation cognitive expert systems.

Don was one of the pioneers the checkers player, and Waterman's. of our field, whose early research built the foundation for the "His subsequent contributions to protocol analysis, to area that would later come to be labeled "knowledge based the technology of rule-based systems, and to the literature of systems" (and still later "expert systems"). Don received a B.S. in Electrical Engineering from With Don's work on production systems in his thesis, it Iowa State University in 1958, and an M.S. in Electrical was only natural that he should move to Carnegie-Mellon to Engineering from the University of California, Berkeley in work with Allen Newell after acquiring his Ph.D. in 1968. He then entered the Ph.D. program at Stanford's Al takes up the story from there: newly created Cotiputer Science Department. While at "Don came to CMU in Psychology, rather than Computer Berkeley he met a young professor named Ed Feigenbaum, Science. As with many people in AI, he had an abiding and when Feigenbaum moved to Stanford in 1965 Don became interest in understanding human cognition, although it always Ed's first Ph.D. student.

The Oak Ridge National Laboratory (ORNL) Center for Engineering Systems Advanced Research (CESAR) is a national center for multidisciplinary long-range research and development (R&D) in machine intelligence and advanced control theory. Intelligent machines (including sensor-based robots) can be viewed as artificially created operational systems capable of autonomous decision making and action. One goal of the research is autonomous remote operations in hazardous environments. This review describes highlights of CESAR research through 1986 and alludes to future plans.

The annual conference of the Association for the Advancement of Artificial Intelligence (AAAI) is the premier U.S. gathering for artificial intelligence (AI) theoreticians and practitioners. On the commercial side, AAAI is the only event with a comprehensive exhibition that includes most significant U.S. vendors of AI products and services. In 1986 some 5100 people attended AAAI- a very good showing considering that the 1987 International Joint Conference on Artificial Intelligence (IJCAI) drew about the same number of people even with its substantial international support. The commercial exhibits at AAAI-86 (110 exhibitors; 80,000 square feet) gave us opportunity to take a snapshot of an industry in transition. What I saw was a dramatic increase in the commercialization of AI technology and a decrease in the mystique, smoke, and hype. A preliminary tour of the AAAI-86 exhibits indicated that participants could expect substantial changes from the situation at IJCAI-85.

AI Magazine, 3

This paper describes a domain-independent implementation of explanation-based generalization (EBG) within a logic-programming environment. Explanation is interleaved with generalization, so that as the training instance is proven to be a positive example of the goal concept, the generalization is simultaneously created. All aspects of the EBG task are viewed in logic, which provides a clear semantics for EBG, and allows its integration into the logic-programming system. In this light operationally becomes a property requiring explicit reasoning. Additionally, viewing EBG in logic clarifies the relation of learning search-control to EBG, and suggests solutions for dealing with imperfect domain theories.

This report contains the reading list for the Qualifying Examination in Artificial Intelligence. Areas covered include search, representation, reasoning, planning and problem solving, learning, expert systems, vision, robotics, natural language, perspectives and AI programming. An extensive bibliography is also provided.

"Knowledge-Based Tutoring describes the advantages and difficulties of adapting an expert system for use in teaching and problem solving. In this case the well-known rule-based expert system, MYCIN, which has been widely used in medical artificial intelligence to do infectious disease diagnosis and therapy selection, is used as a base for the instructional program GUIDON. MYCIN's rules are interpreted by GUIDON in order to evaluate a student's problem solving and provide assistance as the student gathers information about a patient and makes a diagnosis. The book describes what GUIDON does, how it is constructed, and the benefits and limitations of its design."