Ms. Claudia Mazzetti AAAI AAAI has supported small workshops for the last several years. This support has 445 Burgess Drive included publicity, printing, office help, and subsidies for other expenses. Any topic in AI science or technology is appropriate, and anyone may volunteer Submit all proposals to: to organize a workshop on any topic. The organizer(s) should determine Jay M. Tenenbaum, Chair, AAAI Conference the topic, the date, the site, and the procedure for selecting papers and attendees. Committee He or she should also decide whether preprints should be distributed.

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.

To be useful in practice, a factory production schedule must reflect the influence of a large and conflicting set of requirements, objectives and preferences. Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. A variety of constraint-directed inference techniques are defined with respect to this model to provide a basis for intelligently compromising among conflicting concerns. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.

This report describes recent and continuing research in artificial intelligence and related fields being conducted at the University of Pennsylvania. Although AI research takes place primarily in the Department of Computer and Information Science ( in School of Engineering and Applied Science), many aspects of this research are preformed in collaboration with other engineering departments as well as other schools at the University, such as the College of Arts and Sciences, the School of Medicine, and Wharton School.

This report describes recent and continuing research in artificial intelligence and related fields being conducted at the University of Pennsylvania. Although AI research takes place primarily in the Department of Computer and Information Science ( in School of Engineering and Applied Science), many aspects of this research are preformed in collaboration with other engineering departments as well as other schools at the University, such as the College of Arts and Sciences, the School of Medicine, and Wharton School.

I review the research leading from the GUIDON rule-based tutoring system, including the reconfiguration of MYCIN into NEOMYCIN and NEOMYCIN's generalization in the heuristic classification shell, HERACLES. The presentation is organized chronologically around pictures and dialogues that represent conceptual turning points and crystallize the basic ideas. My purpose is to collect the important results in one place, so they can be easily grasped. In the conclusion, I make some observations about our research methodology.

The interpretation of images of postal mail pieces is The Vision Group the domain of this investigation. Our efforts have included It is becoming increasingly important for vision researchers the development of various operators for visual data processing in diverse fields to interact, and the Vision Group at SUNY and image segmentation. The invocation of these Buffalo was formed to facilitate that interaction Current routines and the interpretation of the information they return membership includes 25 faculty and 25 students from 10 is determined by a control structure that uses a variant departments (computer science, electrical and computer of relaxation combined with a rule-based methodology.

THE AI MAGAZINE Summer, 1986 55 building one of the first stored-program digital computers, AI also had its share of controversy, however, at Rand the JOHNNIAC (see Figure 1) (Gruenberger, 1968);l and elsewhere. Given its quick rise to popularity and its George Dantzig and his associates were inventing linear ambitious predictions (Simon & Newell, 1958), AI soon programming (Dantzig, 1963); Les Ford and Ray Fulkerson had its critics, and one of the most prominent, Hubert were developing techniques for network flow analysis Dreyfus, published his famous critique of AI (Dreyfus, (Ford & Fulkerson, 1962); Richard Bellman was developing 1965) while he was consulting at Rand. In addition, the his ideas on dynamic programming (Bellman, 1953); early promise of automatic machine translation of text Herman Kahn was advancing techniques for Monte Carlo from one language to another (the emphasis at Rand was simulation (Kahn, 1955); Lloyd Shapley was revolutionizing on translation from Russian to English) produced only game theory (Shapley, 1951-1960); Stephen Kleene was modest systems, and the goal of fully automated machine advancing our understanding of finite automata (Kleene, translation was abandoned in the early 1960s.

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.

One of the features of a computer program that attempts concept learning is described by the term bias. The bias of a learning program refers to the collection of factors that are brought to bear upon the selection and consideration of partially formed hypotheses pertaining to the concept being learned. For most learning programs, the bias is fixed and provided by the program's author. Utgoff investigates how a learning program may modify its bias by considering when a shift in bias should be attempted, by proposing a method for accomplishing the shift, and by implementing a program that demonstrates procedures for performing a shift. The book begins with an introduction to machine learning and bias and a discussion of related work.