Human error is an increasingly important and addressable concern in modernday high-technology accidents. Avoidable human errors led to many famous accidents, including Bhopal, the space shuttle Challenger, Chernobyl, the Exxon Valdez, and Three Mile Island. Many hundreds of thousands of nonfamous accidents occur each year that are equally or more avoidable. Dramatic examples make the local headlines, such as car crashes, train and plane wrecks, and military-related operations mishaps. Less dramatic consequences happen even more frequently because of millions of mundane errors that appear daily in the products we use (for example, poorly designed cars), the processes we are affected by (for example, banking or healthcare institutions), and the automation that surrounds us (for example, unfriendly computers that expect us to adapt to their interfaces).

The enterprise of developing knowledge-based systems is currently witnessing great growth in popularity. The utility of a well-stocked store of examples and the ability to generate new examples are emphasized. Section 3 displays a wide variety of rule operations in addition to the rule interpreter which applies rules. It includes an argument that systems must be designed to respond well to forces of change. Speech on Artificial Intelligence delivered to the Canadian Information Processing Society, Session '84, Calgary, Alberta An earlier version of this article was published in the Proceedings of the CIPS/Session '84 This article was written while the author was with Rutgers Univcrsity Revision of draft performed at BBN Laboratories Research reported here was supported by NSF Grants Knowledge based systems may be usefully viewed in terms of three interrelated spaces.

To elaborate, suppose a helicopter-based computer vision system is looking at a snow-covered terrain; this terrain knowledge must then be explicitly taken into account in a target recognition procedure. Clearly, the processing required for a snow-covered background is different from that for, say, a wooded area in spring. As a simpler example of knowledgebased processing, consider the problem of self-location for a vehiclemounted vision system (Kak et al. 1987). Let's say the vehicle's whereabouts are approximately known from the position encoders mounted on the wheels, the precision of this information limited by the extent of slippage in the wheels, and so on. Given this approximate information, is it possible to make a more precise fix on the location of the vehicle by integrating the vision data with the map knowledge while the two are out of registration?

In the years since, we have also found that representing knowledge is difficult and time consuming. Building new knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could instead be done by assembling reusable components. System developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, using them to perform some of its reasoning.

In the last few years, we have witnessed a major growth in the use of empirical methods in AI. In part, this growth has arisen from the availability of fast networked computers that allow certain problems of a practical size to be tackled for the first time. There is also a growing realization that results obtained empirically are no less valuable than theoretical results. Experiments can, for example, offer solutions to problems that have defeated a theoretical attack and provide insights that are not possible from a purely theoretical analysis. I identify some of the emerging trends in this area by describing a recent workshop that brought together researchers using empirical methods as far apart as robotics and knowledge-based systems.

The Workshop on the Validation and Verification of Knowledge-Based Systems gathers researchers from government, industry, and academia to present the most recent information about this important development aspect of knowledge-based systems (KBSs). The 1995 workshop focused on nontraditional KBSs that are developed using more than just the simple rule-based paradigm. This new focus showed how researchers are adjusting to the shift in KBS technology from stand-alone rulebased expert systems to embedded systems that use object-oriented technology, uncertainty, and nonmonotonic reasoning. In "Specification Refinement of Object-Oriented KBSs," A. Vermesan (Foundation for Research in Economics and Business Administration, Norway) looks at KBSs that perform reasoning in a framework of structured objects. Her approach is to verify that as details are added to the specification of a KBS, these additions are consistent with the initial abstract specification.

"An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a Course-of-Action Critiquer," by Gheorghe Tecuci, Mihai Boicu, Mike Bowman, and Dorin Marcu, describes a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency's (DARPA) High-Performance Knowledge Bases Program. Murray Burke, the DARPA manager for this program, introduces the article by setting the context for the application. Ontologies also play a key role in the creation and management of a web portal developed by Steffen Staab and his colleagues at the University of Karlsruhe, discussed in their article, "Knowledge Portals: Ontologies at Work." "L As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed applications are systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are systems that are close to deployment and clearly show an innovative implementation of AI technologies. Papers submitted in this track can also describe efforts that examine the utility of different AI techniques for specific applications. All these case studies are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the technical challenges provided by real-world problems. Six deployed applications and 12 emerging application papers were presented plus 2 invited talks. Although no single theme emerges from this panoply of excellent applications, they served to demonstrate that the field continues to be fertile ground for innovation.

This Fall issue marks the first time we have devoted the AI Magazine to a single theme. The idea originated a couple of years ago, and I'm pleased to see the actual implementation. Mark Fox, Special Editor for this issue, is to be congratulated for a fine job of selecting some of the best authorities in the field and working with them to produce an excellent survey of the current state of the art in AI for manufacturing. In fact, Mark exceeded our expectations and solicited more articles than we could reasonably fit in one issue. The quality of all the articles was so high that we didn't want to exclude any of them.

Past Research in Expert Systems at ETSU Artificial intelligence research at East Texas State University (ETSU) began in the fall of 1983 with the development of a knowledge-based expert system to solve configuration problems. The intention was to develop a generic system that could be transferred from one problem domain to another. The problem domains selected on which the system was to be tested were the configuration of Hewlett-Packard Model 29 computer systems and the generation of degree plans for graduate students in the Computer Science Department at ETSU. The configurator is based on a semantic network that utilizes frames as a method of representing knowledge. Frames are used as nodes in the network and can contain facts, rules, and links to other nodes.

Remember that time and space are a priori conditions of human perception in Kant's philosophy. On the one hand, time is inherent to action and change; on the other, action and change are possible because of the passage of time. According to McDermott, "Dealing with time correctly would change everything in an AI program" (McDermott 1982, p. 101). It should not be surprising then that temporal reasoning has always been a very important topic in many fields of AI, particularly areas dealing with change, causality, and action (planning, diagnosis, natural language understanding, and so on). AI developments based on temporal reasoning lead to general theories about time and action, such as McDermott's (1982) temporal logic, Vilain's (1982) theory of time, and Allen's (1984) theory of action and time. Work on the application of these results has taken place in fields such as planning and medical knowledge-based systems. However, action and change are not an exclusive interest of AI.