There has been a good deal of discussion recently about the possibility of standardizing knowledge representation efforts, including the development of an interlingua, or knowledge interchange format (KIF), that would allow developers of declarative knowledge to share their results with other AI researchers. In this article, I examine the practicality of this idea. I present some philosophical arguments against it, describe a straw-man KIF, and suggest specific experiments that would help explore these issues.

The applications include the user-advised construction of an assembly line balancing system and a self-optimizing street light control system. The generalized production-rule strategy that is better than any other at Arizona State University. The estimation is based on for the decision maker to respond to. The system can serve as a module simulation models. of an expert system in need of numeric Figure 1 shows the or functional estimates of hiddenvariable Mazur, Robert F. geographically distributed input Cromp, Bede McCall, operations and knowledge bases. Bickmore, Jan van been in the area of forecasting and Leeuwen, João Martins, interpolating econometric indicators.

The design constructs about the functional aspects of these can be no more general than the Reviewed by Amit Mukerjee prototypes. A harbinger of actions, information that can then be learning and vocabulary inadequacy) change is perhaps the book Knowledge-Based used to refine or adapt the prototype may be why the authors turn to analog Design Systems by R. D. to meet the design goals. Coyne, M. A. Rosenman, A. D. Radford, problem is then reduced to the problem Where the book falls short is in M. Balachandran, and J. S. Gero of searching through these possible illustrating the difference between (Addison Wesley, Reading, Mass., control actions to identify a the design task and other traditional 1990, 567 pages): It presents the sequence that will result in the desired Much of the discussion concentrates view because the volume is based on techniques are used in this process. Some of the other problems encountered here will also planning-type search through a space issues that one would have thought be different. Indeed, it seems in vision, planning, learning, and so resulting in conflicting criteria that clear that a large number of design on.

Task-specific architectures are a growing area of expert system research. Evaluation is one task that is required in many problem-solving domains. This article describes a task-specific, domain-independent architecture for candidate evaluation. I discuss the task-specific architecture approach to knowledge-based system development. Next, I present a review of candidate evaluation methods that have been used in AI and psychological modeling, focusing on the distinction between discrete truth table approaches and continuous linear models. Finally, I describe a task-specific expert system shell, which includes a development environment (Ceved) and a run-time consultation environment (Ceval). This shell enables nonprogramming domain experts to easily encode and represent evaluation-type knowledge and incorporates the encoded knowledge in performance systems.

In my Ph.D. dissertation (Bhansali 1991), I develop an integrated knowledge-based framework for efficiently synthesizing programs by bringing together ideas from the fields of software engineering (software reuse, domain modeling) and AI (hierarchical planning, analogical reasoning). Based on this framework, I constructed a prototype system, APU, that can synthesize UNIX shell scripts from a high-level specification of problems typically encountered by novice shell programmers. An empirical evaluation of the system's performance points to certain criteria that determine the feasibility of the derivational analogy approach in the automatic programming domain when the cost of detecting analogies and recovering from wrong analogs is considered.

This article picks up the call for a reflective examination of the prevailing computational metaphor of AI (and philosophical presuppositions behind it) by sketching alternatives that might serve as seeds for discussion-specifically, the seven alternatives introduced in our previous article (see "AI Magazine, spring 1991). The relative strengths and weaknesses of the alternatives are contrasted with those of the computational metaphor.

Each node has between Joshi), and computational vision (S. Papers were presented by The Department of Electronics, Government under contract with Indian companies. Seven major research and KBCS applications, including expert logic programming (which teaching centers and a number of systems for government administration, seems to be well developed in India), associated institutions are involved expert systems for engineering and reasoning. The level of most presentations are the Center for the Development vision system applications, and was good and of an international of Advanced Computing (Pune), the KBCS applications in and for ancient flavor. The audience was Department of Electronics (New Indian sciences; and language-processing unusually active, initiating discussions Delhi), The Indian Institute of Science technologies, including natural and friendly controversies.

AI research is conducted at a number of academic and research units at the Georgia Institute of Technology. Some of this research is basic in nature, and some has an applied character to it. This article briefly describes basic AI research in the College of Computing at Georgia Tech.

A memorial tribute to AI researcher Jonathan King who died of cancer in 1991.

Case-based reasoning provides both a methodology for building systems and a cognitive model of people. It is consistent with much that psychologists have observed in the natural problem solving people do. Psychologists have also observed, however, that people have several problems in doing analogical or case-based reasoning. Although they are good at using analogs to solve new problems, they are not always good at remembering the right ones. However, computers are good at remembering. I present case-based decision aiding as a methodology for building systems in which people and machines work together to solve problems. The case-based decision-aiding system augments the person's memory by providing cases (analogs) for a person to use in solving a problem. The person does the actual decision making using these cases as guidelines. I present an overview of case-based decision aiding, some technical details about how to implement such systems, and several examples of case-based systems.