Many techniques for representing knowledge have been proposed, but there have been few reports that compare their application This article presents an experimental comparison of four knowledge representation schemes: a simple production system, a structured production system, a frame system, and a logic system. The authors would like to express their appreciation to Dr Edward A. Feigenbaum and H Penny Nii of Stanford University for discussing the early results of this research during their visit to the authors' laboratory The authors also would like to acknowledge the support of Dr Jun Kawasaki, the general manager of Systems Development Laboratory, Hitachi, Ltd This information helps an expert system designer clarify the domain's characteristics and develop a conceptual system design. However, little information is provided for selecting adequate techniques after the system's function (input/output) is determined. The system was designed to interpret the X-ray powder diffraction spectra of rocks to determine their constituent minerals. This article focuses on expert system building tools; however, there may be many cases where no such tools are available.

At a minimum, commercial knowledge engineering tools support the use of rules for knowledge representation. In addition, tools designed for large KBSs also provide other knowledge representation approaches, such as frames, objects, and the ability to extend the knowledge base to support hypothetical reasoning. Rules Rules provide a modular and uniform approach to knowledge representation. Tools that support rules as their sole representation paradigm are relatively simple to learn and use. As rule-based systems grow, however, they become increasingly difficult to understand and maintain (van de Brug, Bachant, and McDermott 1986).

We describe a program for verifying that a set of rules in an expert system comprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOCIN System, a rule-based consultant for clinical oncology The stylized format of ONCOCIN's I ules has allowed the automatic detection of a number of common errors as the knowledge base has been developed This capability suggests a general mechanism for correcting many problems with knowledge base completeness and consistency before they can cause pel fol mancc errors THI? BUILDERS FAKNOWI,EDGE-BASED cxpertsystern must ensure t,hat, t.he system will give its users accurate advice or correct solutions to t,heir problems. The process of verifying that a system is accurate and reliable has two distinct components: checking t,hat the knowledge base contains all necessary information and verifying that the program can interpret, and apply this information correctly. This process involves testing and refining the system's knowledge in order t,o discover and correct a variet.y of errors that, can arise during the process of transferring expertise from a human expert, to a computer syst,em. In this paper, we discuss some common problems in knowledge acquisition and debugging, and describe an aut,omxt,ed assistant for checking t,he completeness and consistency of the knowledge base in the ONCOCIN system (ShortJiffc, 1981).

One of the difficulties of antimicrobial prescribing lies in the necessity to sequentially adjust the treatment of a patient as new clinical data become available. The lack of specialized healthcare resources and the overwhelming amount of information to process make manual surveillance unsustainable. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. However, the highly sensitive knowledge base used by the system leads to many false alerts.

Northrop Research and Technology Center, One Research Park, Pales Wdes Peninsula, CA 90274 It, is interesting t,o note that there is no agreed upon definition of artificial intrlligence. Because government agencies ask for it, software shops claim to provide it, popular magazines and newspapers publish articles about, it, dreamers base their fant,asies on it, and pragmatists criticize and denounce it. Such a stat,c of affairs has persisted since Newell, Simon, and Shaw wrote thcif first. Not knowing exactly what we ale talking about, or expecting is typical of a new field; for example, witness the chaos that centcrcd around program verification of security rclated aspects of systems a few years ago The details are too glim to recount, in mixed company. However, artificial intelligence has been around for nearly 30 years, so one might wonder why our wheels are st,ill spinning.

This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, followup action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately. With AI, all relevant laws and regulations as well as current practices are guaranteed to be considered and followed.

A special session entitled "Future Directions In Artificial Intelligence" was held at the National Conference on Artificial Intelligence in Washington, D.C. in August. The session, chaired by Jack Minker, was held to honor Soviet cyberneticist Alexander Yankelovich Lerner's seventieth birthday. Minker described Dr. Lerner's contributions to science. Participants Saul Amarel, Nils Nilsson, John McCarthy and Patrick Winston gave a technical presentation, followed by questions from the audience. Following the session, 228 attendees signed a letter wishing Dr. Lerner a happy birthday, and 233 attendees signed a petition to be sent to Yuri Andropov of the Soviet Union requesting that Dr. Lerner be given permission to emigrate so that he may join his daughter and her family in Israel.

AI theory and its technology is rarely consulted in attempted resolutions of social problems. Solutions often require that decision-analytic techniques be combined with expert systems. The emerging literature on combined systems is directed at domains where the prediction of human behavior is not required. A foundational shift in AI presuppositions to intelligent agents working in collaboration provides an opportunity to explore efforts to improve the performance of social institutions that depend on accurate prediction of human behavior. Professionals concerned with human outcomes make decisions that are intuitive or analytic or some combination of both.

Workshops The Workshop on Advances in Real-Time Expert System Technologies was held on 3 August 1992 in conjunction with the Tenth European Conference on AI. Participation was limited to invited researchers only. The workshop focused on practical problems occurring during the implementation of real-time expert systems. In this respect, different industrial applications were discussed. The debate covered a wide range of applications, such as qualitative simulation and anytime algorithms for real-time process control.

The workshop "Advances in Interfacing Production Systems with the Real World" was designed to bring together researchers from around the world to focus on the problem of integrating production systems into industrial environments. Nine papers were accepted for the proceedings, and six of them were discussed at the workshop. The workshop "Advances in Interfacing Production Systems with the Real World" was designed to bring together researchers from around the world to focus on the problem of integrating production systems into industrial environments. Nine papers were accepted for the proceedings, and six of them were discussed at the workshop. Real-time knowledge-based systems play a vital role in today's industry.