The first session aimed to set the component being tested. The stage for the day's discussion by focusing variation in all three of these contexts on the issues surrounding the will lead to different types of and Verification of Knowledge-use of formal specification techniques The first paper, by Formal Specifications to Design Intelligence (AAAI-94) in Seattle, Lance Miller of SAIC, was entitled Verifiable Hybrid KBS" by Rose Gamble, Washington, marked the seventh This paper provided a with its specification, and (2) the The 1994 workshop was significant basis for the comparison of validation refinement of formal specifications in that there was a definitive move in and verification techniques to for their implementation. O'Leary, from the lows the possibility of constraining techniques for validating certain University of Southern California, the experts' choices to ensure that properties of KBSs. A paper by presented a paper on the relationship any new knowledge added is valid Alun Preece, Cliff Gossner, and T. between errors and size in KBSs. This and that the knowledge base structure Radhakrishnan (all from the University paper is among the first to address ensures the knowledge is of Aberdeen, Scotland) considered this important issue.

This report stems from a workshop that was organized by the Association for the Advancement of Artificial Intelligence (AAAI) and cosponsored by the Information Technology and Organizations Program of the National Science Foundation. The purpose of the workshop was twofold: first, to increase awareness among the artificial intelligence (AI) community of opportunities presented by the National Information Infrastructure (NII) activities, in particular, the Information Infrastructure and Tech-nology Applications (IITA) component of the High Performance Computing and Communications Program; and second, to identify key contributions of research in AI to the NII and IITA.

My collaborators and I have recently reported in domain science journals several human-computer discoveries in biology, chemistry, and physics. One might ask what accounts for these findings, for example, whether they share a common pattern. My conclusion is that each finding involves a new representation of the scientific task: The problem spaces searched were unlike previous task problem spaces. Such new representations need not be wholly new to the history of science; rather, they can draw on useful representational pieces from elsewhere in natural or computer science. This account contrasts with earlier explanations of machine discovery based on the expert system view. My analysis also suggests a broader potential role for (AI) computer scientists in the practice of natural science.

Analogy has a rich history in Western civilization. Over the centuries, it has become reified in that analogical reasoning has sometimes been regarded as a fundamental cognitive process. In addition, it has become identified with a particular expressive format. The limitations of the modern view are illustrated by monster analogies, which show that analogy need not be regarded as something having a single form, format, or semantics. Analogy clearly does depend on the human ability to create and use well-defined or analytic formats for laying out propositions that express or imply meanings and perceptions. Beyond this dependence, research in cognitive science suggests that analogy relies on a number of genuinely fundamental cognitive capabilities, including semantic flexibility, the perception of resemblances and of distinctions, imagination, and metaphor. Extant symbolic models of analogical reasoning have various sorts of limitation, yet each model presents some important insights and plausible mechanisms. I argue that future efforts could be aimed at integration. This aim would include the incorporation of contextual information, the construction of semantic bases that are dynamic and knowledge rich, and the incorporation of multiple approaches to the problems of inference constraint.

Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.

Moore's technique employs a conventional

In addition to process control, the forum plans to address issues related to evolving technologies, Eastman Kodak (Rochester, N.Y.), a most popular Internet navigator and FDA, EPA, and OSHA compliance, manufacturer of imaging-related products, front-end tools. CERN has chosen safety and environmental concerns, has developed an online neural Harlequin to continue development and application of industry network-based machine vision system and commercialization of its Web standards. KnowledgeBroker (Reno, Nev.), a This Windows-based inspection Ingersoll Milling Machines (Rockford, supplier of expert system-based help system automatically inspects and Ill.), a manufacturer of industrial desk support software, has signed a analyzes the fine-pitch solder paste products, has developed an expert system-based support contract with American physical quality. Following an initial neural network-based optical character schedules and reports on the activities rollout, KnowledgeBroker's HelpNet recognition (OCR) technology, has that need to be completed to successfully 800/900 Service will provide 24-hour signed an agreement to supply IBM install each customer's order. It live technical computer support for Ireland with OCR Readers for AN allows a customer to directly access shrink-wrapped software applications POST, Ireland's national postal service. The Federal Home Loan Mortgage ground data, control center Corp., better known as Freddie Mac BrainTech (Scottsdale, Ariz.), a developer hardware and software, has been of neural network and fuzzy logic-based Diego, Calif.) to use HNC's neural network-based Va.), a developer of acoustic systems La.), an oil refinery, has tested and Contel's subscribers will be able to The new software will allow traffic installed a neural network-based speak a natural stream of continuous controllers in the new $11 million application to control its atmospheric digits to place phone calls from Greater Houston Traffic and Emergency tower.

The 1994 AAAI Robot-Building Laboratory (RBL-94) was held during the Twelfth National Conference on Artificial Intelligence. The primary goal of RBL-94 was to provide those with little or no robotics experience the opportunity to acquire practical experience in a few days. Thirty persons, with backgrounds ranging from university professors to practitioners from industry, participated in the three-part lab.

In its early stages, the field of AI had as its main goal the invention of computer programs having the general problem-solving abilities of humans. Along the way, a major shift of emphasis developed from general-purpose programs toward performance programs, ones whose competence was highly specialized and limited to particular areas of expertise. In this article, I claim that AI is now at the beginning of another transition, one that will reinvigorate efforts to build programs of general, humanlike competence. These programs will use specialized performance programs as tools, much like humans do.

The vertical launch system (vls) tech-assist expert system is being used by the in-service engineering agent as a force multiplier to maintain the readiness, with fewer resources, of a growing population of vlss in the U.S. Navy fleet. This article describes the collaborative development of this knowledge-based system for diagnosis; its main features, including case-based and model-based reasoning; and the lessons we learned from the process.