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.

Robert S. (Bob) Engelmore, who retired in 1998 from the Knowledge Systems Laboratory at Stanford University, died in an ocean accident in Hawaii on March 25, 2003. As the second editor of AI Magazine, he guided its development from 1981 to 1991; he was also elected a fellow of AAAI in 1992. He had been involved in many aspects of AI and was respected for his uncommon common sense and good humor. He played football for Briarcliff Manor High School, learned to play the piano, and most importantly nurtured a deep interest in science. He won a nationally prestigious Westinghouse science scholarship to Carnegie Institute of Technology (later Carnegie Mellon University) and became a physics major.

In the first phase of the analysis, I produced a conceptual reconstruc-Peter D. Karp In the next phase, I searched for patterns in the differences between successive My Ph.D. dissertation describes an A class knowledge base defines a tax-states of the biologists' knowledge. Patfocuses on a program of research in process knowledge base describes the terns in the differences indicate reamolecular biology that culminated in chemical reactions that can occur soning methods that were used to the discovery of a new mechanism of between the biological objects in this derive new theories from old ones. An experiment is described My analysis identified theory-modifiuation. In the first phase of my work, in a third knowledge base by creating cation operators that the biologists I performed a historical study of this the particular objects (instantiated used to modify their theories; these program of biological research in from the known classes of objects) operators form the core of the which I reconstructed the different that are present in the experiment. These patterns also supat different points in time and then called Gensim (genetics simulator) port the conjecture that scientists use analyzed the differences between predicts experimental outcomes by four different modes of scientific these successive theories.