Abstract: Expert systems have recently been arousing much interest in industry and elsewhere: it is envisaged that they will be able to solve problems in areas where computers have previously failed, or indeed, never been tried. However, although the literature in the field of expert systems contains much on their construction, on knowledge representa-tion techniques, etc, relatively little has been devoted to discussing their application to real-life problems. This article seeks to bring together a number of issues relevant to the application of expert systems by discussing their advantages and limitations, their roles and benefits, and the influence that real-life applications might have on the design of expert systems software. Part of the expert systems strategy of one major chemical company is outlined. Because it was in constructing one particular expert system that many of these issues became important this system is described briefly at the start of the paper and used to illustrate much of the later discussion. It is of the plausible-inference type and has application in the field of materials engineering. The article is aimed as much at the interested end-user who has a possible application in mind as at those working in the field of expert systems.

Abstract: Using a collection of facts, rules of thumb, and methods of applying those rules and making inferences, a new type of computer system is emerging: the expert system.

Abstract: This paper describes a system called PDS, a forward chaining, rule-based architecture designed for the online, realtime diagnosis of machine processes. Two issues arise in the application of expert systems to the analysis of sensor-based data: spurious readings and sensor degradation. PDS implements techniques called retrospective analysis and meta-diagnosis as solutions to these problems. These techniques and our experiences in knowledge acquisition in a large organization, and the implementation of PDS as a portable diagnostic tool are described.

Abstract: The author discusses a number of issues that serve as research goals for discovering the principles of knowledge representation, using techniques and concepts evolved while developing the knowledge-representation system KL-one as illustrations. The focus is on what constitutes a good representational system and a good set of representational primitives for dealing with an open-ended range of knowledge domains. Issues of interest include those problems that arise in attempting to construct intelligent computer programs that use knowledge to perform some task. 7 references.

Abstract: ACE, a system for Automated Cable Expertise, is a Knowledge-Based Expert System designed to provide troubleshooting reports and management analyses for telephone cable maintenance. Design decisions faced during the construction of ACE were guided by recent successes in expert systems technology, most notably R1/XC0N, the Digital Equipment Corporation VAX configuration program. ACE departs from "standard" expert system architectures in its use of a conventional data base management system as its primary source of information. Its primary sources of knowledge are the users of the database system and primers on maintenance analysis strategies.

Abstract: : The major line of argument that we will pursue in this paper can be outlined as follows. In Sec. II, we briefly trace the development of the idea of knowledge-based systems in AI. Sec. III is devoted to discussing the increasing need for symbolic content to expert reasoning as the size and demands of the task domain increase; i.e., we will analyze why a complete mathematical model of the situation, even if available, will not meet many of the demands placed on expert reasoning. In Sec. IV, we discuss the several distinct senses and roles that the notion of rules can play and have played in expert systems, and how a failure to keep these separate can cause a great deal of confusion. In Sec. V, we will argue that further organizational constructs, such as concepts and types of problem solving, are needed both to construct more powerful expert systems, and to characterize their capabilities. We will also provide two examples of generic problem-solving types, and show how each type of problem-solving induces an organization of knowledge in the form of a cooperating community of specialists engaged in that problem solving type. The overall flow of the discussion is in the direction of the evolution of expert systems from numerical programs to highly organized symbolic structures engaged in distinct types of problem-solving and communicating with one another.

Abstract: : One common feature of most knowledge-based expert systems is that they must draw conclusions on the basis of evidential information, Yet there is very little agreement on how this should be done. In this paper, the authors present their view of this problem and its solution for multisensor integration. They begin by characterizing evidence as information that is uncertain, incomplete, and sometimes inaccurate. On the basis of this characterization, they conclude that evidential reasoning requires both a method for pooling multiple bodies of evidence to arrive at a consensus and some means of drawing the appropriate conclusions from that consensus. They contrast their approach, which is based on a relatively new mathematical theory of evidence, with those that have their basis in Bayesian probability models. They believe that their method has significant advantages over Bayesian methods in its ability to represent and reason from bounded ignorance. They describe an implementation of these techniques by means of two kinds of memory: long-term memory and short-term memory. This implementation provides for automated reasoning from evidential information at multiple levels of abstraction over time and space.

Abstract: The combined use of Database Management Systems (DBMS)and Artificial Intelligence-based Expert Systems (ES) ispotentially very valuable for modern business applications.The large body of facts usually required in business informationsystems can be made available to an ES through anexisting commercial DBMS. Furthermore, the DBMS itself canbe used more intelligently and operated more efficiently ifenhanced with ES features. However, the implementation ofa DBMS-ES cooperation is very difficult.We explore practical benefits of the cooperative use ofDBMS and ES, as well as the research challenges it presents.Strategies for providing data from a DBMS to an ES are given;complementary strategies for providing intelligence from anES to a DBMS are also presented. Finally, we discuss architechural issues such as degree of coupling, and combinationwith quantitative methods.As an illustration, a research effort at New York University to integrate a logic-based business ES with a relationalDBMS is described.

Abstract: Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. An approach to supporting work in the office is described. Using and extending ideas from the field of artificial intelligence (AI) we describe office work as a problem-solving activity. A knowledge-embedding language called OMEGA is used to embed knowledge of the organization into an office worker's work station in order to support the office worker in problem solving. A particular approach to reasoning about change and contradiction is discussed. This approach uses OMEGA's viewpoint mechanism, which is a general contradiction-handling facility. Unlike other knowledge representation systems, when a contradiction is reached the reasons for the contradiction can be analyzed by the deduction mechanism without having to resort to search mechanisms such as a backtracking. The viewpoint mechanism is the heart of the problem-solving support paradigm, a paradigm which supplements the classical AI view of problem solving. An example is presented in which OMEGA's facilities are used to support an office worker's problem-solving activities. The example illustrates the use of viewpoints and of OMEGA's capabilities to reason about its own reasoning processes. Categories and Subject Descriptors: H.3.4 [ Information Storage and Retrieval ]: Systems and Software— information networks ; H.4.1 [ Information Systems Applications ]: Office Automation; I.2.1 [ Artificial Intelligence ]: Applications and Expert Systems— office automation ; I.2.4 [ Artificial Intelligence ]: Knowledge Representation Formalisms and Methods— semantic networks

Abstract: This sales letter may not influence you to be smarter, but the book that we offer will evoke you to be smarter. Yeah, at least you'll know more than others who don't. This is what called as the quality life improvisation. Why should this build your own expert system? It's because this is your favourite theme to read. If you like this theme about, why don't you read the book to enrich your discussion?

Abstract: Human expertise should be better understood before the users of expert sytems specify the services needed and expected from such systems. An analysis of expert thinking has been developed to assist in this understanding. The analysis is discussed in the paper under three main headings: Specifications : examples are given of the services users obtain from human experts, in the particular domain of petroleum geology. These services indicate general qualities desirable in a human and, by analogy, in a system. The qualities are listed as specifications for expert system design. A theory of expert thinking : how human experts acquire, understand and use their knowledge (particularly with reference to petroleum geology). The theory identifies a number of human, knowledge-handling techniques which could be implemented in a system to meet most of the user's specifications. Human and system expertise : a comparison suggests that, properly designed and suitably applied, an expert system can help its users make well-informed decisions; failing this, the system may prove dangerously misleading and should not be accepted as a substitute for an accountable, human expert.

Abstract: Probabilistic rules and their variants have recently supported several successful applications of expert systems, in spite of the difficulty of committing informants to particular conditional probabilities or ";certainty factors"; and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values. Here we survey recent developments concerning reasoned assumptions which offer hope for avoiding the practical elusiveness of probabilistic rules while retaining theoretical power, for basing systems on the information unhesitatingly gained from expert informants, and reconstructing the entailed degrees of belief later.

Abstract: : We present an approach to expert systems for mechanical design called Design Refinement, which addresses a subset of design activity by using a hierarchy of conceptual specialists that solve the design problem in a disturbed manner, top-down, choosing from sets of design plans and refining the design at each level of the hierarchy.

Abstract: We discuss commercial expert system development, using our experience with the Dipmeter Advisor system as a case study. While the data is too sparse for definitive results, several ideas have emerged as important and suggestive as guidelines for subsequent commercial expert system undertakings. During the past four years, the Dipmeter Advisor system has migrated from an initial experiment in application of expert system techniques in well-log interpretation to a candidate commercial interpretation system. The system has undergone substantial change: It has been implemented in different configurations, in different languages, and on different computer systems. Our ability to experiment with the system has been greatly enhanced by the tools and ideas of rapid prototyping. We have also observed an oscillation in thrust over time between (i) expansion and change in the domain knowledge, and 00 selection and design of appropriate expert system tools. Finally, we have found several of the maxims of expert system development to be valid, but question a number of others.

Abstract: Knowledge-Based Report Generation is a technique for automatically generating natural language summaries from databases. It is so named because it applies the tools of knowledge-based expert systems design to the problem of text generation. The technique is currently being applied to the design of an automatic natural language stock report generator. Examples drawn from the implementation of the stock report generator are used to describe the components of a knowledge-based report generator.

Abstract: The inevitable proliferation of expert systems underscores the need for robust, friendly interfaces requiring minimal user training. The objective of the XCALIBUR project is to meet this need by providing natural comprehension and generation in the context of a focused mixed-initiative dialog. The XCALIBUR architecture is discussed, including its three central components (parser, generator and information manager), its methods of handling ellipsis and imperfect input, and its relation to the underlying expert system.

Abstract: For natural language dialog systems, going beyond domain independence means the attempt to create a core system that can serve as a basis for interfaces to various application classes that differ not only with respect to the domain of discourse but also with respect to dialog type, user type, intended system behavior, and background system. In the design and implementation of HAM ANS. which is presently operational as an interface to an expert system, a vision system and a data base system, we have shown that going beyond domain independence is possible. HAM-ANS is a large natural language dialog system with both considerable depth and breadth, which accepts typed input in colloquial German and produces typed German responses quickly enough to make it practical for real-time interaction. One goal of this paper is to report on the lessons learned during the realization of the system HAM-ANS. This paper introduces the overall structure of the system's processing units and knowledge sources. In addition we describe some of the innovative features concerning the strategy of semantic interpretation.

Abstract: Recent developments in expert systems point toward success for this technology in business environments Expert systems employ unique programming techniques to model expert decisions The production system has thus far proved to be the best programming method for expert knowledge Some expert systems have been verified as performing at an expert level, either scientifically or through field use Some examples of behavioural variables found in a business environment are described Executives who choose to ignore expert systems may find themselves at a competitive disadvantage within the next decade

Abstract: The development of expert systems for troubleshooting digital electronics is considered. It is shown that suitably represented descriptions of structure and behavior set an important foundation. The authors approach offers a unity of device description and simulation, since the descriptions themselves are runnable. Unsolved problems are noted. 10 references.