Search results for '*Expert Systems' (try it on Scholar)

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  1. Jesse F. Dillard & Kristi Yuthas (2001). A Responsibility Ethics for Audit Expert Systems. Journal of Business Ethics 30 (4):337 - 359.score: 240.0
    To effectively pursue ethical action, the business community must recognize that the fundamental form of human association is not the "social contract" into which persons enter as atomic individuals, making partial commitments to each other for the purpose of gaining limited common ends or of satisfying certain laws. The fundamental form of human association is rather the face to face community in which ongoing commitments are the rule and in which aspects of every individual''s experience are conditioned by the continuing (...)
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  2. Gerald Heidegger (1989). Human Experts and Expert Systems: A View From the Shop-Floor. [REVIEW] AI and Society 3 (1):47-57.score: 240.0
    For quite some time, the research in artificial intelligence has focused on expert systems, because here are to be found practical applications at the experimental stage which may soon become widespread. This focus makes more pressing the need to link the debate about the fundamental efficiency of artificial intelligence with those activities that aim at the application of specialized expert systems. In this paper, I begin by considering the stages and the development of human expertise. As a frame (...)
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  3. Randall Whitaker & Olov Östberg (1988). Channeling Knowledge: Expert Systems as Communications Media. [REVIEW] AI and Society 2 (3):197-208.score: 240.0
    Expert Systems (ES) are as yet imperfectly defined. Their two consistently cited characteristics are domain knowledge and expert-level performance. We propose that current structural definitions are inadequate and suggest a view of ES as communication channels. We proceed to explore the factors influencing applicability of ES technology to an enterprise and the impacts that could be expected. A consequence of this view is the idea of incremental information loss on the path from the expert to the ES user. Strategies (...)
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  4. Marvin J. Croy (1989). Ethical Issues Concerning Expert Systems' Applications in Education. AI and Society 3 (3):209-219.score: 240.0
    This article traces the connection between expert systems used as consultants in medicine and their design for instructional purposes in education. It is suggested that there are important differences between these applications. Recognizing these differences leads to the view that the development of intelligent computer-assisted instructions (ICAI) should be guided by empirical research into social/psychological consequences and by ethical inquiries into the acceptability of those consequences. Three proposals are put forward: (1) that the pedagogical role of intelligent CAI be (...)
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  5. Thomas Hermann & Katharina Just (1995). Experts' Systems Instead of Expert Systems. AI and Society 9 (4):321-355.score: 240.0
    By studying several cases of expert systems' use, a variety of difficulties were identified as directly depending on specific characteristics of experts and their tasks. This concerns more than the questions: “May experts be replaced by machines?” or “Is experts' knowledge explicable?”. The organisational structure of their work as well as the cyclic, non-plannable way of their task performing have further relevance. The paper introduces the concept of experts' systems to deal with diversities of their expertise and complexities (...)
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  6. Peter Schefe (1990). The Impacts of Expert Systems on Working Life — An Assessment. AI and Society 4 (3):183-195.score: 240.0
    Expert systems provide new languages and a new methodology for automating knowledge-intensive processes. Whilst the benefits expected are ubiquitously stated, probable negative impacts are seldom admitted by the dominant actors in the field. We deal with probable problematic impacts on employment as well as contents and structure of work both in production and the service and administration areas and make some suggestions concerning measures to be taken to account for these impacts assuming no radical change as to the prevailing (...)
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  7. Edgar A. Whitley (1991). Two Approaches to Developing Expert Systems: A Consideration of Formal and Semi-Formal Domains. [REVIEW] AI and Society 5 (2):110-127.score: 240.0
    The conventional approach to developing expert systems views the domain of application as being “formally defined”. This view often leads to practical problems when expert systems are built using this approach. This paper examines the implications and problems of the formal approach to expert system design and proposes an alternative approach based on the concept of semi-formal domains. This approach, which draws on the work of socio-technical information systems, provides guidelines which can be used for the design (...)
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  8. Barbara Gorayska & Kevin Cox (1992). Expert Systems as Extensions of the Human Mind: A User Oriented, Holistic Approach to the Design of Multiple Reasoning System Environments and Interfaces. [REVIEW] AI and Society 6 (3):245-262.score: 240.0
    Expert systems have had little impact as computing artifacts. In this paper we argue that the reason for this stems from the underlying assumption of most builders of expert systems that an expert system needs to acquire information and to control the interaction between the human user and itself. We show that this assumption has serious linguistic and usability flaws which diminish the likelihood of producing socially acceptable expert systems. We propose a reversal of this paradigm, for (...)
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  9. Barrie Lipscombe (1989). Expert Systems and Computer-Controlled Decision Making in Medicine. AI and Society 3 (3):184-197.score: 240.0
    The search for “usable” expert systems is leading somemedical researchers to question the appropriate role of these programs. Most current systems assume a limited role for the human user, delegating situated “decision-control” to the machine. As expert systems are only able to replace a narrow range of human intellectual functions, this leaves the programs unable to cope with the “constructivist” nature of human knowledge-use. In returning practical control to the human doctor, some researchers are abandoning focusedproblem-solving in (...)
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  10. Peter P. Mykytyn Jr (1989). Decision Making, Computer Attitudes and Expert Systems: What is Our Direction? [REVIEW] AI and Society 3 (2):133-141.score: 240.0
    Expert systems have been concerned with applications dealing with medical diagnosis, mineral exploration, and computer configuration, with some efforts relatively successful in achieving results at least as good as human experts. Today, much is being written about these systems and managerial decision-making activities in organizations and the positive impact that they can have in these situations. However, it appears that expert systems could become somewhat of a panacea for some organizational ailments as research, development, and marketing of (...)
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  11. Dr Peter Senker, Joe Townsend & Joanna Buckingham (1989). Working with Expert Systems: Three Case Studies. [REVIEW] AI and Society 3 (2):103-116.score: 240.0
    Three case studies were conducted on the implications of the use of expert systems for the work of clerks and operators in Britain. An expert system had been introduced in a process control application. The operators' work was deskilled. The second case was a fault diagnosis application. An operator was very happy with his new work. In the third case, insurance clerks received training to operate an expert system which extended the scope of their work. In conclusion, it is (...)
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  12. Elke Steven, Michael Hoenen & Matthias Kloth (1992). Testability of Expert Systems in System Development and Application. AI and Society 6 (4):337-344.score: 240.0
    In this paper the difficulties arising out of a necessary examination of expert systems as to the ‘correctness’ of functioning are outlined. The argumentation is based on the problematic use of the knowledge term in expert system development and the design perspectives connected with the cognitivistic knowledge concept. It becomes obvious that fundamental problems in system development will involve negative consequences for utilization. The perspective developed from this analysis is assuming that these problems have to be taken into account (...)
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  13. Evans E. Woherem (1991). Human Factors in Information Technology: The Socio-Organisational Aspects of Expert Systems Design. [REVIEW] AI and Society 5 (1):18-33.score: 240.0
    This paper looks beyond the mostly technical and business issues that currently inform the design of knowledge-based systems (e.g., expert systems) to point out that there is also a social and organisational (a socio-organisational) dimension to the issues affecting the design decisions of expert systems and other information technologies. It argues that whilst technical and business issues are considered before the design of Expert Systems, that socio-organisational issues determine the acceptance and long-run utility of the technology (...)
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  14. Janet S. Zeide & Jay Liebowitz (1992). Institutionalizing Expert Systems: Guidelines and Legal Concerns. [REVIEW] AI and Society 6 (3):287-293.score: 240.0
    Often, knowledge engineers become so involved in the development process of the expert system that they fail to look further down the road toward the expert system's institutionalization within the organization. Institutionalization is an important component of the expert system planning process. More specifically, the legal issues associated with expert systems development and deployment are critical institutionalization factors. This paper looks at some expert system institutionalization guidelines, and then focuses on legal considerations.
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  15. Dr Joseph A. Cannataci (1989). Law, Liability and Expert Systems. AI and Society 3 (3):169-183.score: 232.0
    This paper examines some of the possible legal implications of the production, marketing and use of expert systems. The relevance of a legally useful definition of expert systems, comprising systems designed for use both by laymen and professionals, is related to the distinctions inherent in the legal doctrine underlying provision of goods and provision of services. The liability of the sellers and users of, and contributors to, expert systems are examined in terms of professional malpractice as (...)
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  16. Giorgio Sacchi (1994). Social Logics and Expert Systems. AI and Society 8 (1):84-87.score: 210.0
    My goal is to emphasize the way we generally use the word ‘logic’ and the sort of problems related to the definition of logic and the sort of problems related to the definition of logic. I also wish to underline the differences between human intelligence and artificial intelligence.
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  17. Brian P. Bloomfield (1988). Expert Systems and Human Knowledge: A View From the Sociology of Science. [REVIEW] AI and Society 2 (1):17-29.score: 208.0
    After the setbacks suffered in the 1970s as a result of the ‘Lighthill Report’ (Lighthill, 1973), the science of Artificial Intelligence (AI) has undergone a dramatic revival of fortunes in the 1980s. But despite the obvious enormity and complexity of the problems tackled by AI, it still remains rather parochial in relation to the import of alternative though potentially fruitful ideas from other disciplines. With this in mind, the aim of the present paper is to utilise ideas from the sociology (...)
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  18. Emma Rooksby (2009). How to Be a Responsible Slave: Managing the Use of Expert Information Systems. [REVIEW] Ethics and Information Technology 11 (1):81-90.score: 204.0
    Computer ethicists have for some years been troubled by the issue of how to assign moral responsibility for disastrous events involving erroneous information generated by expert information systems. Recently, Jeroen van den Hoven has argued that agents working with expert information systems satisfy the conditions for what he calls epistemic enslavement. Epistemically enslaved agents do not, he argues, have moral responsibility for accidents for which they bear causal responsibility. In this article, I develop two objections to van den (...)
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  19. Daniel E. O'Leary & Robert M. O'Keefe (1997). The Impact of Artificial Intelligence in Accounting Work: Expert Systems Use in Auditing and Tax. [REVIEW] AI and Society 11 (1-2):36-47.score: 192.0
    This paper uses Perrow’s sociological framework as a basis for a comparative organisation analysis of the impact of expert systems on organisational issues. The study analyses the relative impact of expert systems on two different types of accounting work: auditing and tax. The results indicate an impact on factors that ultimately improve productivity. The aggregate results indicate that expert systems are found to allow the user substantial control of search for solutions and discretion on whether to follow (...)
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  20. Anja Oskamp (1992). Model for Knowledge and Legal Expert Systems. Artificial Intelligence and Law 1 (4):245-274.score: 180.0
    This paper presents a four layer model for working with legal knowledge in expert systems. It distinguishes five sources of knowledge. Four contain basic legal knowledge found in published and unpublished sources. The fifth consists of legal metaknowledge. In the model the four basic legal knowledge sources are placed at the lowest level. The metaknowledge is placed at levels above the other four knowledge sources. The assumption is that the knowledge is represented only once. The use of metaknowledge at (...)
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  21. Ronald K. Stamper (1991). The Role of Semantics in Legal Expert Systems and Legal Reasoning. Ratio Juris 4 (2):219-244.score: 180.0
    The consensus among legal philosophers is probably that rule-based legal expert systems leave much to be desired as aids in legal decision-making. Why? What can we do about it? A bureaucrat administering some set of complex rules will ascertain the facts and apply the rules to them in order to discover their consequences for the case in hand. This process of deductive reasoning is characteristically bureaucratic.
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  22. Roger M. Cooke (1986). Probabilistic Reasoning in Expert Systems Reconstructed in Probability Semantics. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:409 - 421.score: 180.0
    Los's probability semantics are used to identify the appropriate probability conditional for use in probabilistic explanations. This conditional is shown to have applications to probabilistic reasoning in expert systems. The reasoning scheme of the system MYCIN is shown to be probabilistically invalid; however, it is shown to be "close" to a probabilistically valid inference scheme.
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  23. Willard Downs & Kelley Ann Newton (1989). Legal Implications in Development and Use of Expert Systems in Agriculture. Journal of Agricultural and Environmental Ethics 2 (1):53-58.score: 180.0
    Applications of Artificial Intelligence, particularly Expert Systems, are rapidly increasing. This science promises to give computer-based systems the capability of reasoning and decision making in near human-like fashion. Whether used for farm management or intelligent machine control, Expert Systems will find many agricultural applications. Much of the development and distribution of such systems will probably take place in the public sector, particularly the Cooperative Extension Service. A major nontechnical factor affecting the development and extensive use of (...)
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  24. Peter Hucklenbroich (1988). Problems of Nomenclature and Classification in Medical Expert Systems. Theoretical Medicine and Bioethics 9 (2).score: 180.0
    Medical expert systems (MES) are knowledge-based computer programs that are designed for advising physicians on diagnostical and therapeutical decision-making. They use heuristic methods developed by Artificial Intelligence researchers in order to retrieve from large knowledge-bases information needed in the situation. Constructing the knowledge-base of a MES embraces the problem of explicating and fixing the conceptual, causal and epistemic relations between a lot of medical objects. There is a number of preconditions which any adequate representation of such knowledge must fulfil, (...)
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  25. Ronald Stamper, James Backhouse & Karl Althaus (1987). Expert Systems: Lawyers Beware! Theoria 3 (1):317-340.score: 180.0
    Two fundamental paradigms are in conflict. Expert systems are the creation of the artificial intelligence paradigm which presumes that an objective reality can be understood and controlled by an individual expert intelligence that can be replaced by machinery. The alternative paradigm assumes that reality is the subjective product of human beings striving to collaborate through shared norms and experiences, a process that can be assisted by but never replaced by computers. The first paradigm is appropriate in the domains of (...)
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  26. Kris van Koppen & David Goldsborough (1990). Information Technology in Municipal Environmental Policy: Automated Registration, Sure, but What About Expert Systems? [REVIEW] Knowledge, Technology and Policy 3 (3):91-98.score: 180.0
    Dutch municipalities are confronted with an increased number of prescribed environmental tasks and also with a growing demand, both from the central government and environmental pressure groups, to undertake environmental activities on their own initiative. This development over-taxed the information management of most municipalities. In the past few years, computer technology was introduced to relieve part of this pressure (e.g., by automation of registration systems). In this article we present a classification of computer applications for environmental management, investigate their (...)
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  27. Ian White (1988). W(H)Ither Expert Systems? — A View From Outside. AI and Society 2 (2):161-171.score: 176.0
    The paper questions the expert system paradigm, both in terms of its range of application, and as a significant contribution to the understanding of artificial intelligence. The viewpoint is that of the systems designer who must judge the applicability of these methods in imminent and future systems. The expert system paradigm, (ESP for short), is criticised not because it is ubiquitously wrong, but because its range of application appears to be very limited, and much promise is made of (...)
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  28. Pål Sørgaard (1991). Evaluating Expert System Prototypes. AI and Society 5 (1):3-17.score: 160.0
    There is a disparity between the multitude of apparently successful expert system prototypes and the scarcity of expert systems in real everyday use. Modern tools make it deceptively easy to make reasonable prototypes, but these prototypes are seldom made subject to serious evaluation. Instead the development team confronts their product with a set of cases, and the primary evaluation criterion is the percentage of correct answers: we are faced with a “95% syndrome”. Other aspects related to the use of (...)
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  29. Jay Liebowitz & Francisco J. Cantu-Ortiz (1992). Expert System Technology Transfer Strategies: Selected Cases From the United States and Mexico. [REVIEW] AI and Society 6 (4):324-336.score: 160.0
    Expert systems are being developed in a multitude of domains worldwide. The usage of expert systems within organizations is growing; however, many expert systems projects still fail due to poor ‘institutionalization’ practices. This paper addresses various strategies for providing the transfer of expert systems technology within organizations. Specifically, this paper will address expert system technology transfer strategies using examples from United States and Mexican organizations.
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  30. Hubert L. Dreyfus (1985). From Socrates to Expert Systems: The Limits and Dangers of Calculative Rationality. In Carl Mitcham & Alois Huning (eds.), Philosophy and Technology II: Information Technology and Computers in Theory and Practice. Reidel.score: 156.0
    Actual AI research began auspiciously around 1955 with Allen Newell and Herbert Simon's work at the RAND Corporation. Newell and Simon proved that computers could do more than calculate. They demonstrated that computers were physical symbol systems whose symbols could be made to stand for anything, including features of the real world, and whose programs could be used as rules for relating these features. In this way computers could be used to simulate certain important aspects intelligence. Thus the information-processing (...)
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  31. Ron Sharpe, Jacek Gibert & Stephen Oakes (forthcoming). Expert Systems and Artificial Intelligence Applications in Engineering Design and Inspection. 8th Int Conf. On Industrial and Engrg Applications of Ai and Expert Sys., International Society of Applied Intelligence (Isai).score: 156.0
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  32. Toomas Tuba IkaIn & John W. Grlesser (forthcoming). Expert Systems Catching on at the Navy Finance Center. Ai Systems in Government Conference: Proceedings.score: 156.0
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  33. Kevin D. Ashley (1992). Case-Based Reasoning and its Implications for Legal Expert Systems. Artificial Intelligence and Law 1 (2-3):113-208.score: 150.0
    Reasoners compare problems to prior cases to draw conclusions about a problem and guide decision making. All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases. Across domains and tasks, however, humans reason with cases in subtly different ways evidencing different mixes of and mechanisms for these components.In (...)
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  34. Layman E. Allen, Sallyanne Payton & Charles S. Saxon (1990). Synthesizing Related Rules From Statutes and Cases for Legal Expert Systems. Ratio Juris 3 (2):272-318.score: 150.0
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  35. Roberto J. Vernengo (1991). Decision Forms and Expert Systems in Law. Ratio Juris 4 (2):245-252.score: 150.0
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  36. Kimberly Cass (1996). Expert Systems as General-Use Advisory Tools. Business and Professional Ethics Journal 15 (4):61-85.score: 150.0
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  37. Janet Vaux (2001). From Expert Systems to Knowledge-Based Companies: How the AI Industry Negotiated a Market for Knowledge. Social Epistemology 15 (3):231 – 245.score: 150.0
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  38. Mark Alfino, Do Expert Systems Have a Moral Cost?score: 150.0
    When professionals are asked about the value of information technology to their work, they typically give two kinds of answers. Some see the advent or arrival of sophisticated information technology as a great boon to their professional lives. For them, the only question is how soon can the technology be deployed to open up new horizons for professional activity and end dull and tedious work. Others sense more acutely the serious..
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  39. Joseph S. Fulda (1988). The Logic of Expert Judging Systems and the Rights of the Accused. AI and Society 2 (3):266-269.score: 150.0
  40. Neil MacCormick (1992). Legal Deduction, Legal Predicates and Expert Systems. International Journal for the Semiotics of Law - Revue Internationale de Sémiotique Juridique 5 (2):181-202.score: 150.0
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  41. Giulianella Coletti, Angelo Gilio & Romano Scozzafava (1991). Conditional Events with Vague Information in Expert Systems. In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. 106--114.score: 150.0
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  42. Ejan Mackaay, Daniel Poulin, Jacques Frémont, Paul Bratley & Constant Déniger (1990). The Logic of Time in Law and Legal Expert Systems. Ratio Juris 3 (2):254-271.score: 150.0
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  43. Donald Michie (1985). Current Developments in Artificial Intelligence and Expert Systems. Zygon 20 (4):375-389.score: 150.0
  44. Julia Barragán (1993). Juridical Argumentation and Expert Systems in Law. Rechtstheorie 3:61-69.score: 150.0
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  45. Mark Alfino (1993). Information Ethics in the Workplace: Do Expert Systems Have a Moral Cost? Journal of Information Ethics 2 (2):15-19.score: 150.0
     
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  46. J. Barragan (1993). The Theory of Argument and the Refinement Process in Legal Expert Systems. Rechtstheorie 24 (3):317-328.score: 150.0
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  47. Dr Michael D. Coovert, Kathleen McNelis, Kamesh Ramakrishna & Eduardo Salas (1989). Preferences for Power in Expert Systems by Novice Users. AI and Society 3 (1):59-61.score: 150.0
  48. K. Dockx & R. Timmermans (forthcoming). Adding a Temporal Dimension to Expert Systems Working in a Real-Time Environment. Communication and Cognition—Artificial Intelligence.score: 150.0
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  49. Joseph S. Fulda (1994). An Application of Resolution to Expert Systems: Overcoming Schoenmakers' Paradigm. Association for Automated Reasoning Newsletter 25:10-12.score: 150.0
    The full-text of the entire issue is available on the Web; readers seeing this should ensure that there is permission to download. It would be quite difficult to separate just my piece from the others.
     
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  50. Yoshimitsu Hirai (1989). Trends in the Development and Application of Expert Systems in Japan: 1986 to 1988. [REVIEW] AI and Society 3 (4):357-364.score: 150.0
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