PROLOG can compute quantities as answers by extract,ing values from the variable bindings imroduced in the proof of p from S, and so serves as a general purpose programming language. Logical programming languages attract many people in artificial intelligence because of the relative ease of stating declarative information in them, as compared with traditional programming languages Since most knowledge-based, expert systems contain large numbers of essentially declarative statements, the designers of the FGC expect their choice of PROLOG to facilitate the construction and operation of knowledge-based systems Parallelism enters the picture because traditional PROLOG requires that all sentences be expressed in clausal form, searches for proofs of its goal by examining the input clauses in a fixed linear order, and within clauses, examining literals in left-to-right order Many of these imposed orderings have no purely logical basis, so that, as far as questions of deducibility are concerned, greater efficiency may be possible with separat,e deduction searches conducted concurrently. In such a reorganization of PROLOG, time of execution is ideally proportional to the depth of the proof found (the size of the answer), rather than proportional to the number of alternative proofs (the size of the search space). Does S t-p? may be needlessly interest to economists is not affected by such a change. In the second, ordinal utilities were abandoned in, favor of sets of binary preferences among alternatives.

What Is a Knowledge Representation? Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it--What is it?--has Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have.

This is Part 2 of a two-part article and discusses issues of tort liability and the use of computers in the courtroom. Part 1 of this article, which appeared in the Spring 1988 issue of AI Magazine, discussed steps that developers of AI systems can take to protect their efforts, and the attendant legal ambiguities that must eventually be addressed in order to clarify the scope of such protection. Part 2 explores the prospect of AI systems as subjects of litigation. Once inside the courtroom, what role can the computer assume in its own defense or in the service of some other litigant? The law of evidence, developed to govern the testimony of human witnesses, must continually evolve to accommodate new, nonhuman sources of information.

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness to a user This paper introduces the Interviewer/Reasoner model, which helps to reduce this conflict This model partitions an intelligent system into two asynchronous components The Interviewer's primary function is to gather data while providing an acceptable response time to the user The Reasoner does most of the symbolic computation for the system This paper describes the implementation of the model in both timesharing and personal workstat,ion environments, and uses the ONCOCIN system as an example The work described in t,his paper was carried out at Stanford University and was partly supported by the National Library of Medicine under program project grant LM-00395. The original idea for splitting the tasks of information gathering from reasoning in order to improve system response time was suggested by Ted Shortliffe and Chuck Clanton for the ONCOCIN project Thanks are due to Eric Schoen and Bill van Melle for help with the implementation, to Mark Stefik and Harold Brown for help in writing this paper, and to the rest of the ONCOCIN project members, including Carli Scott, Miriam Bischoff, Charlotte Jacobs, and Craig Tovey. An acceptable response time is needed both during system testing and to help insure end-user acceptability. During the normal course of development of an AI system there is substantial t,esting on real problems under the guidance of human experts whose time is usually valuable. Moreover, many end users (e.g., physicians) will simply refuse to use a system if they have to wait for a response.

The International Workshop on Processing Declarative Knowledge was held in Kaiserslautern, Germany, from 1 to 3 July 1991. The workshop was intended as a forum for the presentation of new approaches to processing declarative knowledge, the discussion of procedural versus alternative paradigms, and the issues concerned with efficient processing of realistic knowledge bases. Demonstrations of implemented systems were also announced.

A hybrid fuzzy knowledge-based system with crisp and fuzzy rules as well as numerical methods was developed for multiobjective optimization of power distribution system operation. The development process and knowledge-acquisition process for the fuzzy knowledge-based system are described in detail. Fuzzy sets are defined for recent temperature trend, line section loading, transformer aging, voltage-level guidelines, and the degree of desirability of a proposed switching combination. After a heuristic preprocessor proposes a list of switch openings that would seem to reduce system losses, network radiality rules consider whether to open a particular switch and find a corresponding switch that can be closed to maintain radiality. Network parameter rules determine whether the proposed switching combination will violate network integrity.

The goal of intelligent computer-aided engineering (ICAE) is to construct computer programs that capture a significant fraction of an engineer's knowledge. Today, ICAE systems are a goal, not a reality. This article attempts to refine that goal and suggest how to get there. We begin by examining several scenarios of what ICAE systems could be like. Next we describe why ICAE won't evolve directly from current applications of expert system technology to engineering problems.

Problem-solving techniques such as modeling, simulation, optimization, and network analysis have been used extensively to help agricultural scientists and practitioners understand and control biological systems. By their nature, most of these systems are difficult to quantitatively define. Many of the models and simulations that have been developed lack a user interface which enables people other than the developer to use them. As a result, several scientists are integrating knowledgebased-system (KBS) technology with conventional problem-solving techniques to increase the robustness and usability of their systems. To investigate the similarities and differences of leading scientists' approaches, a pioneer workshop, supported by the American Association for Artificial Intelligence (AAAI) and the Knowledge Systems Area of the American Society of Agricultural Engineers, was held in San Antonio, Texas, on 10-12 August 1988.

This article discusses the First International Symposium on Integrating Knowledge and Neural Heuristics, held on 9 to 10 May 1994 in Pensacola, Florida. The highlights of the event are summarized, organized according to the five areas of concentration at the conference: (1) integration methodologies; (2) language, psychology, and cognitive science; (3) fuzzy logic; (4) learning; and (5) applications. This trend has begun to pick up its momentum since the late 1980s, and both approaches have enjoyed many successful applications to real-world problems. This hybrid idea is largely a consequence of an increasingly strong belief that knowledge and neural models can complement each other beneficially. The growing community in this area convened at the First International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH) on 9 to 10 May 1994 in Pensacola Beach, Florida, for the first time on the international level.

Delivering effective customer service over the internet requires attention to many aspects of knowledge management if it is to be both satisfying for customers and economical for the company or other organization. One of the major organizational functions that is still in the early stages of being delivered by the internet is customer service, that is, remedying complaints or providing answers to a particular audience. This task involves many aspects of knowledge management, at least if it is to be convenient and satisfying for customers as well as efficient and inexpensive for the company or organization. On a basic level, it is essential (but not sufficient) to handle the administrative overhead of tracking incoming questions and complaints, together with outgoing responses, over different channels such as email, web forms, and live chat. Beyond this, to support customer service representatives (CSRs), and to assist customers seeking help at peak load times or after hours, it is necessary to provide both a knowledge base containing needed information and a convenient, intuitive means of accessing this knowledge base. Even were it not for the expense of maintaining a large staff of CSRs always available, it is found that many people prefer to find answers to their questions directly on the internet rather than take the time to compose a sufficiently detailed email message or wait in a telephone queue, possibly playing tag with a CSR for days before resolving their concerns. Furthermore, CSRs can experience boredom and burnout from constantly handling similar questions; in many cases, they are not using their skills most efficiently. The most common and straightforward response to this situation is to write and make available on a web page or pages a set of answers to frequently asked questions (FAQs). Such a web page provides a basic solution to the problems mentioned earlier, but except in the simplest and most static cases, it requires continued expert maintenance to keep the FAQ list current and organized. In addition, if the number of FAQs surpasses a few dozen, it becomes difficult for users to navigate the FAQ pages to find the answers they seek. At the opposite end of the sophistication scale, a number of conversational interfaces to knowledge bases have recently appeared, which can be personified as human or character "chatbots" or represented more soberly as simple input-and-response text fields.