Review of Expert Micros

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 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.

An important issue in achieving acceptance of computer systems used by the nonprogramming community is the ability to communicate with these systems in natural language. Often, a great deal of time in the design of any such system is devoted to the natural language front end. An obvious way to simplify this task is to provide a portable natural language front-end tool or facility that is sophisticated enough to allow for a reasonable variety of input; allows modification; and, yet, is easy to use. It allows for user input to be in sentence or nonsentence form or both, provides a detailed parse tree that the user can access, and also provides the facility to generate responses and save information.

In 1940, a 20-year-old science fiction fan from Brooklyn found that he was growing tired of stories that endlessly repeated the myths of Frankenstein and Faust: Robots were created and destroyed their creator; robots were created and destroyed their creator-ad nauseum. So he began writing robot stories of his own. "[They were] robot stories of a new variety," he recalls. The young writer's name, of course, was Isaac Asimov (1964), and the robot stories he began writing that year have become classics of science fiction, the standards by which others are judged.

We note with sorrow the passing of Don Waterman, who died on January 4, 1987. Don was one of the pioneers of our field, whose early research built the foundation for the area that would later come to be labeled "knowledge based systems" (and still later "expert systems").

As a book about search, it is thorough, at the state of the art, and contains expositions that will delight the expert with their clarity and depth. However, it is not, per se, a book about AI (nor was it intended to be) or about the history, philosophy, or cognitive aspects of heuristic knowledge.

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. 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.

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.