Southwest Research Institute and the U.S. Air Force Materiel Command designed and developed an automated system for the preparation of deficiency report analysis information reports ( Engineers and equipment specialists responsible for the troublesome part, or end item, review the MDR to identify the possible cause(s) of failure. In the past, engineers and equipment specialists have turned to operations research (OR) analysts to assist in item performance analysis. This analysis is usually time consuming and personnel intensive and requires information from many Air Force data systems. At the Oklahoma City Air Logistics Center (ALC), located at Tinker Air Force Base, data collection and analysis require two person-days. This document describes an item's SOURCE DATA: The data used to prepare this report came from the following sources: 1) Product Performance Subsystem (G099), 2) Supportability analysis Forecasting Evaluation (SAFE), 3) Flying Hours (G099), 4) MICAP Hours (D165B), and 5) VAMOSC (D160B).

Don was one of the pioneers of our field, whose early research built the foundation for the area that would later come to be labeled "knowledge based systems" (and still later "expert systems"). Don received a B.S. in Electrical Engineering from Iowa State University in 1958, and an M.S. in Electrical Engineering from the University of California, Berkeley in 1964. He then entered the Ph.D. program at Stanford's newly created Cotiputer Science Department. While at Berkeley he met a young professor named Ed Feigenbaum, and when Feigenbaum moved to Stanford in 1965 Don became Ed's first Ph.D. student. Ed recalls: "In mid-1965 the DENDRAL project began in earnest, and Don was its first (and at the time its only) Ph.D. student.

This research was motivated by the widely held belief that constructing an automatic program synthesis system that can accept a high-level description of a problem for an arbitrary domain and generate code for the problem completely automatically is pragmatically impossible. However, by focusing on a well-defined domain, it is possible to incorporate sufficient knowledge within a system so that it can communicate with an end user at the level of his(her) application and automatically generate a program from a problem specification. Such knowledge-based systems often employ a catalog of transformational rules that progressively refine an abstract specification into a concrete implementation. A major research issue in such systems is how to increase the efficiency of the systems by controlling the application of rules and avoiding repetitive traversal of the search space. 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 The knowledge base consists of three subcomponents: a concept dictionary, a library of reusable components, and a layered rule base.

The traditional reliability design methods are imperfect because the designed systems aim at fewer faults, but once a fault happens, the systems might hard fail. To solve this problem, we present a self-maintenance machine (SMM), one that can maintain its functions flexibly even though faults occur. To achieve the capabilities of diagnosing and repair planning, a model-based approach that uses qualitative physics was proposed. Regarding the repair-executing capability, a control-type repair strategy was followed. A prototype of the SMM was developed, and it succeeded in maintaining its functions if the structure did not change.

It is a pleasure to acknowledge the ovel all guidance and suppol t for development of the demonstration work station by Daniel Wiener of the Joint, Tactical Fusion Progl am Office Danicl Vcntimiglia of the Rome Air Development Ccntel provided guidance and support fol the work station demonst,ration, and Capt,ain Richard Radcliffe of the Joint Tactical Fusion I'roglam OIlice provided thr dcvclopmcnt, and testing of the correlation tuner The processor aut omatically merges reports and stores descriptions of each detected item Operat,ols query the files constructed by the correlation processor, and the displays respond in formats selected by the operators The operators use these displays t,o deduce t,hc nat,ure and location of significant cncmy deployments By monitoring changes in the enemy deployments they try to anticipate enemy initiatives so they can help t,hcir commander count,er them. The fusion system did what it was designed to do: It, demonstrated the correlation, query, and display capabilities in a test bed in 1981, and the European Command, 1J.S. Forces has deployed the system in a limitedoperational capabilit,y mode. What, has yet to be dcmonstrated is that, these processes enable analysts to help a commander outwit an enemy. The crucial question is no longer whet,hcr sensor reports can be rapidly correlated, but rather how well humans can sort, through large amounts of correlated sensor data t,o assess situations rapidly and accurately. The same is presumably true of all other command and control syst,em elements that depend on human skill.

Decision analysis and knowledge-based expert systems share some common goals. Both technologies are designed to improve human decision making; they attempt to do this by formalizing human expert knowledge so that it is amenable to mechanized reasoning. However, the technologies are based on rather different principles. Decision analysis is the application of the principles of decision theory supplemented with insights from the psychology of judgment. Expert systems, at least as we use this term here, involve the application of various logical and computational techniques of AI to the representation of human knowledge for automated inference.

Databases are at the heart of most realworld knowledge base systems. The management and effective use of these databases will be the limiting factors in our ability to build ever more complex AI systems. This article reports on a workshop that explored how databases and their associated technologies can best be used in the development of large AI applications. On 26 August 1988, approximately 50 people assembled at a workshop sponsored by the American Association for Artificial Intelligence (AAAI) to share ideas about the use of database management techniques in large AI systems. The organizers of the workshop were Forouzan Golshani, Department of Computer Science, Arizona State University, chairman; Ron Ashany, Department of Computer Science, University of California, Berkeley; Michael Brodie, GTE Labs., Waltham, Massachusetts; Oris Friesen, Bull HN Information Systems, Phoenix, Arizona; Sara Graves, Department of Computer Science, University of Alabama, Huntsville; and Carlo Zaniolo, MCC, Austin, Texas.

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. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems (Green et al., 1974; Lenat et al., 1983; Lenat & Brown, 1984; Schank & Abelson, 1977) have convinced us that each of these approaches has difficulty "scaling up" for want of a substantial base of real world knowledge.

The ability to map the state of an object into a category languages is transforming AI theories into symbolic strucin a classification hierarchy has long been an important tures. This pattern can be seen in knowledge representapart of many fields, for example, biology and medicine. Gordon and Shortliffe, 1985), and has been especially concerned with applying classification to diagnostic problems. One of the problems in classification is that the relationship between observable evidence and categories is often ambiguous. A piece of evidence can be associated with several categories or can occur with a category in an irregular fashion.

The fundamental premise... concerns the need for a strong model of a domaintrained knowledge engineer ... to be embedded in a questionasking system... The user is defined here as a nonprogramming domain expert. An expert, in turn, is an individual with some know-how in a given domain. Consider the following: Scene 1: A person walks into a barbershop and asks for a haircut. What sort of haircut does the barber give?