Abstract: Work on expert systems has received extensive attention recently, prompting growing interest in a range of environments. Much has been made of the basic concept and of the rule-based system approach typically used to construct the programs. Perhaps this is a good time then to review what we know, asses the current prospects, and suggest directions appropriate for the next steps of basic research. I'd like to do that today, and propose to do it by taking you on a journey of sorts, a metaphorical trip through the State of the Art of Expert Systems. We'll wander about the landscape, ranging from the familiar territory of the Land of Accepted Wisdom, to the vast unknowns at the Frontiers of Knowledge. I guarantee we'll all return safely, so come along....

Abstract: An expert system is defined and its basic structure is discussed. The knowledge base, the inference engine, and uses of expert systems are discussed. Architecture is considered, including choice of solution direction, reasoning in the presence of uncertainty, searching small and large search spaces, handling large search spaces by transforming them and by developing alternative or additional spaces, and dealing with time. Existing expert systems are reviewed. Tools for building such systems, construction, and knowledge acquisition and learning are discussed. Centers of research and funding sources are listed. The state-of-the-art, current problems, required research, and future trends are summarized.

Abstract: To date, user participation in the reasoning processes of expert systems has been largely limited to probing expert reasoning or adding limited information. The user may only ask why the system requested more information and how it arrived at its advice. Research into extending the capabilities of expert systems [1,2,3] has so far failed to recognize the need to permit the full range of interactions possible when humans engage in the normal giving and getting of advice. input and human expert response.

Abstract: This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

Abstract: From the Publisher: The Third Edition, is thoroughly updated and contains new material on expert systems, data flow diagrams, processing cycles, and structured analysis and design techniques. Includes study cases and extensive figures and examples to aid in learning. Other new material includes enhanced coverage of such topics as relational databases, LANs, microcomputer-based systems, and more. Contains extensive review problems at the end of each chapter, and is suitable for more than one term if desired.

Abstract: This paper follows a trend towards more user oriented design approaches to interactive computer systems. The implicit goal in this trend is the development of more “natural” systems. Design approaches should aim at a system capable of continuous change by means of suitable agents. The term “soft facade” is used to describe a modifiable interface to a system. Facades vary in softness and agents for change can be the systems designer, the user or an expert system in the facade. In the latter case, the system is called a self-adaptive interface system. The conditions where a self-adaptive interface system is desirable are briefly considered and discussed in relation to a simple example. Recent research in artificial intelligence is mentioned as a possible source of proposed components for a self-adaptive interface system.

Abstract: The acquisition of expert knowledge is fundamental to the certain of expert systems. The conventional approach to building expert systems assumes that the knowledge exists, and that it is feasible to find an expert who has the knowledge and can articulate it in collaboration with a knowledge engineer. This article considers the practice of knowledge engineering when these assumptions can not be strictly justified. It draws on our experiences in the design of VLSI design methods, and in the prototyping of an expert assistant for VLSI design. We suggest methods for expanding the practice of knowledge engineering when applied to fields that are fragmented and undergoing rapid evolution. We outline how the expanded practice can shape and accelerate the process of knowledge generation and refinement. Our examples also clarify some of the unarticulated present practice of knowledge engineering.

Abstract: This short paper Dresents a new algorithm for minimun information Bayesian Inferencing within Expert Systems. This algorithm is as efficient in both time and space as previously reported work [3 3 but always provides a minimum information result. In addition to describing the new algorithm, we will prove that it does indeed satisfy minimum information criteria. Since both algorithms are sub stantially different from the "Bayesian" approaches in well known expert systems such as the original Prospector [1], AL/X [8], and MYCIN [9 3, and from the approach of Kulikowski [5], background is provided to show the motivation for using the minimum information approach to Bayesian updating.

Abstract: This paper describes the design and implementation of an expert system that provides novice users with help in using the Vax/VMS operating system. The most interesting feature of our advisor is that it follows the user’s interactions with the system and volunteers its help when it believes that the user would benefit from advice. The user need not ask for help or raise an error condition. The advisor recognizes correct yet inefficient command sequences and helps the beginner become more proficient by indicating how these tasks may be done more efficiently.

Abstract: An overview of artificial intelligence (AI), its core ingredients, and its applications is presented. The knowledge representation, logic, problem solving approaches, languages, and computers pertaining to AI are examined, and the state of the art in AI is reviewed. The use of AI in expert systems, computer vision, natural language processing, speech recognition and understanding, speech synthesis, problem solving, and planning is examined. Basic AI topics, including automation, search-oriented problem solving, knowledge representation, and computational logic, are discussed.

Abstract: Production rule schemes have proven quite effective in concisely representing expert knowledge in several application areas. Yet, there are many problems for which one would like to take advantage of additional knowledge that may not be easily represented in these surface level models. One class of problems of particular practical interest are those in which we would like to have a computer-based system give interactive advice on how to control and interpret results from a set of complex and interrelated applications programs. The advice may refer to interpretations of current results, possible experiments that should be performed with the help of the applications programs, and indications of inconsistencies in specific analytical procedures and in problem solving sequences followed by the user. In the present paper we report on our experiences in designing an expert system (ELAS), of the type described above, for well log analysis in oil exploration and production. We have integrated a production rule advice model (using the EXPERT system) with existing Amoco software for well-log analysis and display. In doing so, the original system for well-log analysis was reorganized so that its use could be monitored and controlled, and its knowledge structured according to the types and sequences of methods used by expert analysts. By varying the assumptions and parameters used in the different individual analyses, our goal is to make available interactive interpretations of the alternative approaches that an expert might take to a complex problem of well-log analysis.

Abstract: This paper describes the central features of a system designed for the management of large amounts of application specific knowledge. The Knowledge Manager(KM-I) employs distinct software and hardware processors to implement: A file of general knowledge and an associated reasoning engine A file of specific knowledge and an associated searching engine We present our reasons for believing why this can be an effective strategy for realizing many practical knowledge based/expert system applications that lie in a large overlapping area between practical AI and advanced data management technology. We then outline the major features and components of the system and discuss the range of intended applications.

Abstract: An expert system is an automated consulting system which provides the user with expert advice within a particular domain. We briefly review some of the recent literature pertaining to the development and uses of expert systems. In particular, we discuss the design and implementation of an expert system which we have developed to guide a teacher/diagnostician through the various stages of diagnosing reading difficulties.

Abstract: The purpose of this paper is to report on one user's experience with several of the software tools for building an expert system. To the best of the author's knowledge, this is the first time a number of different expert system building tools have been applied to a single problem by a single analyst. A similar single problem/different tool experiment was performed at the Expert Systems Workshop in 1980, but each tool was used by its own proponents on the given problem.

Abstract: Possibilities of application of Artificial Intelligence (AI) to Exp oratory Data Analysis (EDA) are discussed. Expert systems as understood in AI appear very suitable for automated consultations for users of statistical software packages as well as for simulation of EDA as an intelligent activity. This is documented on two small (and implemented) systems and one big project (GUHA-80) in development.

Abstract: SEEK is a system which has been developed to give interactive advice about rule refinement during the design of an expert system. The advice takes the form of suggestions for possible experiments in generalizing or specializing rules in an expert model that has been specified based on reasoning rules cited by the expert. Case experience, in the form of stored cases with known conclusions, is used to interactively guide the expert in refining the rules of a model. The design framework of SEEK consists of a tabular model for expressing expert-modeled rules and a general consultation system for applying a model to specific cases. This approach has proven particularly valuable in assisting the expert in a domain where two diagnoses are difficult to distinguish. Examples are given from an expert consultation system being developed for rheumatology. 12 references.

Abstract: During the quarter century since the birth of the branch of computer science known as artificial intelligence (AI), much of the research has focused on developing symbolic models of human inference. In the last decade several related AI research themes have come together to form what is now known as "expert systems research." In this paper we review AI and expert systems to acquaint the reader with the field and to suggest ways in which this research will eventually be applied to advanced medical monitoring.

Abstract: The authors discuss knowledge-based signal processing-a term used to describe systems that tightly integrate artificial intelligence (AI) and signal processing which attempts to combine techniques from the two disciplines more imaginatively than in the past Researchers in the signal processing community are increasingly becoming aware that combining the architecture and methodology of AI with more traditional tools and techniques can lead to significant advances

Abstract: Efficient syllabic hypothesization in continuous speech has been so far an unsolved problem. A novel solution based on the extraction of acoustic cues is proposed in this paper. This extraction is performed by parallel processes implementing an expert system represented by a grammar of frames.

Abstract: Recent advances in microtechnology will have a significant impact on both Computer Aided Learning and Instruction. They will enable cheap systems to be configured for use in learning situations and will provide a sound basis for improved interface design. This paper first reviews the changes that have taken place and concludes that distributed systems based on standard network technology will become widespread and a new generation of users will emerge who are unskilled in computing and remote from professional advice. The system itself will have to provide guidance. Advances in Computer Aided Learning and Instruction and the work on Expert Systems in Artificial Intelligence will provide a basis for the design of guidance systems which the paper groups under the term Computer Aided Guidance. Such systems will have short-term goals and will be economically justifiable. The paper suggests that workers in Computer Aided Learning and Instruction should contribute to this new field of activity.

Abstract: Fifth Generation Computer Systems, if successfully developed, will be excellent vehicles for Expert Systems applications. Fundamental is a software methodology known as Knowledge Engineering. Knowledge, not inference, is the key to high levels of performance of Expert Systems. A considerable variety of Expert Systems applications, most having great potential payoff, are already being worked on. Scientific innovations in knowledge acquisition will be required. Innovations leading to an efficient industrial technology for Knowledge Engineering will also be necessary. Managers must exhibit imagination, and the willingness to take technological risks to realize the enormous potential benefit from Fifth Generation systems.