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.

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.

Review of Artificial Intelligence for Microcomputers: The Guide for Business Decision Makers

Hillis's book, like its bibliography, has something to offer nearly everyone in the computer science community, even those with no background in computer architecture. The book provides a glimpse of what the next generation of computers will be like.

The objectives of this document (a part of a retrospective monograph on the AGE Project currently in preparation) are (1) to define what is meant by blackboard systems and (2) to show the richness and diversity of blackboard system designs. In Part 1 we discussed the underlying concept behind all blackboard systems -- the blackboard model of problem solving. We also traced the history of ideas and designs of some application systems that helped shape the blackboard model. In application systems, the blackboard system components are integrated into the domain knowledge required to solve the problem at hand.

This article contains reports from the various research groups in the SUNY Buffalo Department of Computer Science, Vision Group, and Graduate group in Cognitive Science. It is organized by the different research topics. However, it should be noted that the individual projects might also be organized around the methodologies and tools used in the research, and, of course, many of the projects fall under more than one category.

This article presents a summary of past and current artificial intelligence research within the Computer Science Department at East Texas State University (ETSU). The Computer Science Department at ETSU offers a master of science degree with an emphasis in artificial intelligence. AI research, both past and present, has been funded by a grant from E-Systems, Greenville Division. Other computing facilities available for artificial intelligence research are four workstations, each providing up to 20 users with LISP and PROLOG interpreters.

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.

Founded in 1972 to develop and disseminate new ideas in computer science, the Information Sciences Institute (ISI) is an off-campus research center of the University of Southern California, with a combined research and support staff of over one hundred. The Institute engages in a broad set of research and application-oriented projects in the computer sciences. The Institute AI research focuses on program synthesis user interfaces, programming environments, natural language, and expert systems. AI researchers are supported by ten personal Lisp workstations, several VAXs, two TOPS-20 systems, and a magnificent view of Marina del Rey.

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.