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  • 1
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 1-19 
    ISSN: 8756-7016
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
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  • 2
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 69-89 
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    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
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  • 3
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 21-35 
    ISSN: 8756-7016
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
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  • 4
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 91-118 
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    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 37-68 
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    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 119-145 
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    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 187-229 
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  • 8
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 231-267 
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  • 9
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 269-290 
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    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 317-358 
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 359-400 
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  • 12
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 401-449 
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  • 13
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 451-467 
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  • 14
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 147-186 
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  • 15
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 469-520 
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  • 16
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 521-556 
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  • 17
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    Palo Alto, Calif. : Annual Reviews
    Annual Review of Computer Science 2 (1987), S. 291-316 
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  • 18
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    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Program debugging is an important part of the domain expertise required for intelligent tutoring systems that teach programming languages. This article explores the process by which student programs can be automatically debugged in order to increase the instructional capabilities of these systems. The research presented provides a methodology and implementation for the diagnosis and correction of nontrivial recursive programs. In this approach, recursive programs are debugged by repairing induction proofs in the Boyer-Moore logic. The induction proofs constructed and debugged assert the computational équivalence of student programs to correct exemplar solutions. Exemplar solutions not only specify correct implementations but also provide correct code to replace buggy student code. Bugs in student code are repaired with heuristics that attempt to minimize the scope of repair.The automated debugging of student code is greatly complicated by the tremendous variability that arises in student solutions to nontrivial tasks. This variability can be coped with, and debugging performance improved, by explicit reasoning about computational semantics during the debugging process. This article supports these claims by discussing the design, implementation, and evaluation of Talus, an automatic debugger for LISP programs, and by examining related work in automated program debugging.Talus relies on its abilities to reason about computational semantics to perform algorithm recognition, infer code teleology, and to automatically detect and correct nonsyntactic errors in student programs written in a restricted, but nontrivial, subset of LISP. Solutions can vary significantly in algorithm, functional decomposition, role of variables, data flow, control flow, values returned by functions, LISP primitives used, and identifiers used. Solutions can consist of multiple functions, each containing multiple bugs. Empiricial evaluation demonstrates that Talus achieves high performance in debugging widely varying student solutions to challenging tasks.
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  • 19
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    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Machine instructional planners use changing and uncertain data to incrementally configure plans and control the execution and dynamic refinement of these plans. Current instructional planners cannot adequately plan, replan, and monitor the delivery of instruction. This is due in part to the fact that current instructional planners are incapable of planning in a global context, developing competing plans in parallel, monitoring their planning behavior, and dynamically adapting their control behavior. In response to these and other deficiencies of instructional planners a generic system architecture based on the blackboard model was implemented. This self-improving instructional planner (SUP) dynamically creates instructional plans, requests execution of these plans, replans, and improves its planning behavior based on a student's responses to tutoring. Global planning was facilitated by explicitly representing decisions about past, current, and future plans on a global data structure called the plan blackboard. Planning in multiple worlds is facilitated by labeling plan decisions by the context in which they were generated. Plan monitoring was implemented as a set of monitoring knowledge sources. The flexible control capability for instructional planner was adapted from the blackboard architecture BB1. The explicit control structure of SUP enabled complex and flexible planning behavior while maintaining a simple planning architecture.
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  • 20
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    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper compares certain aspects of situation semantics and Montague grammar and points out some issues related to natural language programming. It provides and introduction to certain basic concepts of situation semantics and makes some tentative claims about possible advantages of situation semantics.
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  • 21
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    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 3 (1987), S. 0 
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  • 22
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    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 3 (1987), S. 0 
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  • 23
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    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 3 (1987), S. 0 
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  • 24
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    Computational intelligence 3 (1987), S. 0 
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    Computational intelligence 3 (1987), S. 0 
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  • 26
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    Computational intelligence 3 (1987), S. 0 
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  • 27
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    Computational intelligence 3 (1987), S. 0 
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  • 28
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    Computational intelligence 3 (1987), S. 0 
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    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
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    Topics: Computer Science
    Notes: This paper synthesizes a number of approaches to concept representation and learning in a multilayered model. The paper emphasizes what has been called similarity-based learning (SBL) from examples, although this review is extended to address wider issues. The paper pays particular attention to requirements for incremental and uncertain environments, and to interrelationships among concept purpose, concept representation, and concept learning.One goal of the paper is to unite some of the notions underlying recent research, in an attempt to construct a more complete and extensible framework. This framework is designed to capture representations and methods such as those based on hypothesis search and bias selection, and to extend the ideas for greater system capability. This leads to a specific perspective for multilayered learning which has several advantages, such as greater clarity, more uniform learning, and more powerful induction.The approach clarifies and unifies various aspects of the problem of concept learning. Some results'are (1) Various concept representations (such as logic, prototypes, and decision trees) are subsumed by a standard form which is well suited to learning, particularly in incremental and uncertain environments; (2) Concept learning may be enhanced by exploiting a particular phenomenon in many spaces-this phenomenon is a certain kind of smoothness or regularity, one instance of which underlies the similarity in SBL systems; (3) The paper treats the phenomenon in a general way and applies it hierarchically. This has various advantages of uniformity. For example the model allows layered learning algorithms for concept learning all to be instantiations of one basic algorithm. A single kind of representation (an instantiation of the standard form) is prominent at each level. The combination of representation and algorithm allows fast, accurate, concise, and robust concept learning.
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    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
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    Topics: Computer Science
    Notes: This paper presents an approach to learning to control a dynamic physical system. The approach has been implemented in a program named CART, and applied to a simple physical system studied previously by several researchers. Experiments illustrate that a control method is learned in about 16 trials, an improvement over previous learning programs.
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    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
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    Topics: Computer Science
    Notes: A formal, foundational approach to autonomous knowledge acquisition is presented. In particular, “learning from examples” and “learning from being told” and the relation of these approaches to first-order representation systems are investigated. It is assumed initially that the only information available for acquisition is a stream of facts, or ground atomic formulae, describing a domain. On the basis of this information, hypotheses expressed in set-theoretic terms and concerning the application domain may be proposed. As further instances are received, the hypothesized relations may be modified or discarded, and new relations formed. The intent though is to characterize those hypotheses that may potentially be formed, rather than to specify the subset of the hypotheses that, for whatever reason, should be held.Formal systems are derived by means of which the set of potential hypotheses is precisely specified, and a procedure is derived for restoring the consistency of a set of hypotheses after conflicting evidence is encountered. In addition, this work is extended to where a learning system may be “told” arbitrary sentences concerning a domain. Included in this is an investigation of the relation between acquiring knowledge and reasoning deductively. However, the interaction of these approaches leads to immediate difficulties which likely require informal, pragmatic techniques for their resolution. The overall framework is intended both as a foundation for investigating autonomous approaches to learning and as a basis for the development of such autonomous systems.
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    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
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    Topics: Computer Science
    Notes: In this paper we present a computational theory of human motor performance and learning. The theory is implemented as a running AI system called MAGGIE. Given a description of a desired movement as input, the system generates simulated motor behavior as output. The theory states mat skills are encoded as motor schemas, which specify the positions and velocities of a limb at selected points in time. Moreover, there exist two natural representations for such knowledge; viewer-centered schemas describe visually perceived behavior, arid joint-centered schemas are used to generate behavior. When the model acts upon these two representational formats, they exhibit quite different behavioral characteristics. MAGGIE performs the desired movement within a feedback control paradigm, monitoring for errors and correcting them when it detects them. Learning involves improving the joint-centered schema over many practice trials; this reduces the need for monitoring. The model accounts for a number of well-documented motor phenomena, including the speed-accuracy trade-off and the gradual improvement in performance with practice. It also makes several testable predictions. We close with a discussion of the theory's strengths and weaknesses, along with directions for future research.
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    Computational intelligence 3 (1987), S. 0 
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    Topics: Computer Science
    Notes: This paper describes a novel approach to machine learning, based on the principle of learning by reasoning. Current learning systems have significant limitations such as brittleness, i.e., the deterioration of performance on a different domain or problem and lack of power required for handling real-world learning problems. The goal of our research was to develop an approach in which many of these limitations are overcome in a unified, coherent and general framework. Our learning approach is based on principles of reasoning, such as the discovery of the underlying principle and the recognition of the deeper basis of similarity, which is somewhat akin to human learning. In this paper, we argue the importance of these principles and tie the limitations of current systems to the lack of application of these principles. We then present the technique developed and illustrate it on a learning problem not directly solvable by previous approaches.
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    Expert systems 4 (1987), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Book reviewd in this article: The Encyclopedia of Artificial Intelligence. (two volumes). Ed. S. C. Shapiro und D. Eckroth. Expert Systems 1986, Volume 1: the USA and Canada. J. Hewett and R. Susson, Ovum, 1986. Commercial Expert Systems in Europe. J. Newett, S. Timms and G. d'Aumale, Ovum, 1986.
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    Expert systems 4 (1987), S. 0 
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    Notes: Abstract: Recognising the existence of different forms of knowledge is a first step towards effective knowledge elicitation. This article takes a brief look at some of the different types of knowledge which human experts possess and then focusses on the problem of implicit knowledge.The fact that much of an expert's knowledge is implicit or tacit in nature is a major problem for those working in the area of knowledge elicitation. Despite this, the topic has attracted little discussion or research. The present article reviews some of the limited literature on the topic and attempts to settle some of the confusion over what implicit knowledge is, or might be. Relevant experiments from the psychological literature are discussed. The paper also looks at possible ways of assessing implicit knowledge and makes recommendations for future research in this area.
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    Expert systems 4 (1987), S. 0 
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    Topics: Computer Science
    Notes: Abstract: The nature of expertise required in evaluating an economic entity's solvency position is investigated. The decision perspective chosen is that of the independent auditor as s/ he ponders the appropriate opinion to render on the financial statements of an entity in financial distress. Verbal protocols were collected from two Big Eight public accounting firm audit partners with experience in making this type of decision. A comprehensive, two part case was designed reflecting the information available to the auditor at separate two points in time. The protocol analysis yielded several levels of representation. The first was a general model of the opinion decision which, in effect, suggested that a ‘normative model’ of the entity under evaluation was constructed and then compared with the actual information about the entity. The process appeared somewhat akin to medical diagnosis. The next level of analysis involved the general classifications of technical knowledge which represented primitive or intermediate decisions within a hierarchical branching structure. Seven major technical model classifications were identified. The next level was referred to as the IF-THEN rule categories representing decision expertise. General categories included knowledge of business, knowledge of accounting/auditing and current state indicators. These represent the general framework within which the technical knowledge was instantiated. Five primary category frequencies were identified: comparison, evaluation, scenario, rule and recall/review.
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    Notes: Abstract: In this paper we take up the plight of the programmer of a rule based language. Our focus is on the type of development environment that is most supportive of such programmers. Our view will be that programming is programming, whether it be with a rule based, functional or imperative language. While it is true that rule based languages have strong links to the Expert Systems field, our discussion in this paper has less to do with ‘expert systems’ per se/〉, and more to do with the view of rule based languages as yet another computational paradigm, often included under the same roof with non-rule-based languages. Just as programming environment research has progressed for non-rule-based languages, we would like to build more powerful environments in the rule based world as well. We report here on an attempt to build such an environment./〉
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    Notes: Abstract The purpose of knowledge representation for an expert system is to specify functions to be performed by the system. In this paper, a knowledge representation scheme which mutually combines procedures, functions, production rules and Horn clauses is outlined. Its knowledge representation model is an imaginary organisation for performing functions of a target system, where a number of members try to solve given problems systematically. Knowledge is distributed to each of the members with considerable modularity. Functional specification of expert systems would be performed with less difficulty.
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    Expert systems 4 (1987), S. 0 
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    Notes: Xi Plus is an expert systems development tool supplied by Expertech Ltd. It costs $1,250 (in the United States) or $1,250 (in Europe), and is intended for IBM-compatible personal computers. It may be obtained from: Expertech Ltd., Expertech House, 172 Bath Road, Slough SL1 3XE, England. Tel: 0753 821321.
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    Notes: KEE is produced by IntelliCorp Inc., 1975 El Camino Real West, Mountain View, CA 94040-2216, USA and IntelliCorp Ltd., Runnymede Malthouse, Runnymede Road, Egham, Surrey, TW20 9BO, England.
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    Expert systems 4 (1987), S. 0 
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    Notes: Abstract This is the second part of a report which looks at expert systems and the man-machine interface (mmi). The term ‘mmi’ is interpreted in a fairly broad sense to include knowledge acquisition and cognitive aspects of the user interface. Part One of the report (published in the previous issue of Expert Systems) examines what is currently happening in the area of knowledge acquisition in Britain and asks whether it really is the major bottleneck in the production of expert systems.Part Two of the report looks at cognitive aspects of the user interface, including dialogue control, explanation facilities, user models, natural language processing and the effects of new technology. It also considers the very important question of evaluation. Again the report is concerned with what is actually happening in these areas in Britain today.
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    Expert systems 4 (1987), S. 0 
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    Topics: Computer Science
    Notes: Abstract: Opus is a software package for selecting activation processes that are the best suited for a hydrocarbon field. It integrates an expert system to manage what is known in the field and to organise the application of a set of algorithmic programs for making technico-economic evaluations. This article gives a detailed description of the expert system part as well as an example of how the complete system is used.
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    Notes: Abstract: Experts typically solve problems in narrow domains (e.g. the diagnosis of congenital heart diseases). Consultants, however, have expertise that is based on an ability to apply expertise across domains. Such expertise is exhibited in most fields. In this paper we report an investigation of consultant expertise in statistical experimental design. Our analysis reveals that consultants are successful because of their ability to 1) formulate the client's problem in abstract terms, 2) refine and modify this formulation through interaction with the client, and 3) incorporate the client's domain knowledge into an effective problem solution.
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    Notes: Abstract: Expert systems still lack the skill of an expert when it comes to providing explanations of the results of expert reasoning. This is because while such systems may implement knowledge which is sufficient to mimic the performance of an expert, they do not necessarily model the expertise upon which that performance is based. Such a model must include knowledge of that domain's terminology, knowledge of domain facts, and knowledge of problem-solving methods. The Explainable Expert Systems project has been exploring a new paradigm for expert system development that is intended to capture such missing knowledge and make it available for explanation. This paper will discuss the principles behind this paradigm and consider two systems that employ it.
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    Notes: Abstract: This paper describes an inference system which lends itself to graphical representation. An implementation of the system is described, and its application in a legislation based domain is illustrated. The methodology for knowledge elicitation which the system is intended to support is briefly indicated. The algorithm is described, and semantics for the system are given.
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    Notes: Abstract: Component design and tool engineering functions are two sequentially related activities. Many additional benefits may arise if these activities can be effectively integrated together within a central environment. The principal aim of the research described in this paper is to develop a component design and tool engineering Intelligent Knowledge Based System (IKBS), called Holdex to demonstrate such a concept. It is an implementation designed to deduce the planning, design, tooling, procurement and scheduling information at the component design stage. It also assists the component designers in the operation of a Computer Aided Design (Cad) system and leads them towards the rationalisation of resources by encouraging standardisation of designs. A major benefit to arise from the provision of such assistance is to facilitate the tasks of executing the production engineering functions. The range of functions performed by Holdex includes the specification of a set of cutting tools; planning a set of pre-machining requirements; recommendation of the appropriate machine tool and manning level; and the automated design of fixtures. Towards this end, this research aims to provide a foundation for enabling industry to manufacture with the least amount of manual interpretation.
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    Notes: Abstract Expert systems are becoming increasingly complex and diverse. The need for efficient data management for their growing knowledge bases is apparent. Moreover, with the widespread use of Database Management Systems, the operational data required by the expert system is often already available from an online database. Hence the interaction of expert systems and databases is a topic which is receiving increasing attention. This paper reviews existing techniques for such interaction and describes a novel dictionary–driven approach to the problem, called a Dictionary Interface for Expert Systems and Databases (Difead). A prototype of Difead is described, which couples a Medical Diagnosis System, written in Lisp, with a relational database.
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    Notes: Abstract: Experts solve problems. One significant aspect of problem solving is the approach chosen to solve a problem. An often employed approach, but little studied and understood, is the use of analogical thinking. Experts often see current problems in the light of previous problems, and the application of an analogy (i.e. solving a new problem by using an analogous previous problem-solution) can lead to improved problem solving in some instances, and poorer problem solving in others. Recent research in cognitive psychology has led to greater understanding of the analogy process, and work in artificial intelligence has helped to automate what we know about analogies—yet still much is unknown about how analogies develop, how they can be automated, and the role of analogies in expertise.This article describes a continuing research effort to learn more about analogical problem solving, particularly from a viewpoint of automating an analogical ‘inference engine”. Building upon previous work in this field of study, results of our own empirical work concerning the nature of analogies—especially as used by experts—are portrayed in a series of conceptual level descriptions. We have studied experts in several domains, including systems analysis, computer programming, and in management. Next, our efforts to create automated analogy support systems is indicated. Finally, experiments being conducted to tie together expert systems and our analogy support system are discussed. The analogical inference engine being developed is aptly called ‘ThinkBack”.
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    Notes: An increasing number of people are becoming users of unfamiliar software. They can be genuinely “new” computer users or part of a growing group who are transferring skills and knowledge from a familiar product such as a word processor to a functionally similar, but different, unfamiliar one. The problem for users in this position is that they do not have access to training courses to teach them how to use such software and are usually forced to rely on text-based documentation. LIY is a method for producing computer-based tutorials to teach the user ofasoftware product.This paper describes how LIY is, in turn, (1) a method for application system design which recognizes the need for tutorial design (a task analysis and user interface specification provide information structures that are passed to the tutorial designer); (2) a support environment for the tutorial designer (in addition to prompting for courseware for nodes in the task analysis, LIY provides a ready-made rule base for constraining the degree of learner control available while the tutorial is in use. The designer is able to tailor this rule base for a specific tutorial); and (3) a tutorial delivery environment (the tutorial adapts to individual learners and offers a degree of learner control).
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    Notes: Knowledge representation remains a serious issue for researchers of intelligent tutoring systems. Two areas of knowledge representation that are particularly difficult are domain and teaching knowledge. This article discusses and gives example solutions to these knowledge engineering issues and also addresses issues that relate to up-scaling existing intelligent tutoring technology to practical levels so that tutoring systems can be brought into the real world.
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    Notes: The major problem with using standard first-order logic as a basis for knowledge representation systems is its undecidability. A variant of first-order tautological entailment, a simple version of relevance logic, has been developed that has decidable inference and thus overcomes this problem. However, this logic is too weak for knowledge representation and must be strengthened. One way to strengthen the logic is to create a hybrid logic by adding a terminological reasoner. This must be done with care to retain the decidability of the logic as well as its reasonable semantics. The result, a stronger decidable logic, is used in the design of a hybrid, decidable, logic-based knowledge representation system.
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    Notes: A fundamental computational limit on automated reasoning and its effect on knowledge representation is examined. Basically, the problem is that it can be more difficult to reason correctly with one representational language than with another and, moreover, that this difficulty increases dramatically as the expressive power of the language increases. This leads to a tradeoff between the expressiveness of a representational language and its computational tractability. Here we show that this tradeoff can be seen to underlie the differences among a number of existing representational formalisms, in addition to motivating many of the current research issues in knowledge representation.
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    Notes: According to Webster, cohesion is “the act or process of sticking together tightly.” Here the term represents the underlying forces that drive the formation of classes during inductive learning. This paper considers several numerical and conceptual induction algorithms, and compares their methods of cohesion. While these algorithms represent several different methods, they also exhibit some significant commonalities.
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    Notes: One of the most important ways in which an information-provider can assimilate an information-seeking dialogue is by inferring the underlying task-related plan motivating the information-seeker's queries. This paper presents a strategy for hypothesizing and tracking the changing task-level goals of an information-seeker and building a model of his task-related plan as the dialogue progresses.Naturally occurring utterances are often imperfect. The information-provider often appears to use acquired knowledge about the information-seeker's underlying task-related plan to remedy many of the information-seeker's faulty utterances and enable the dialogue to continue without interruption. This paper presents a strategy for understanding one kind of defective utterance. Our approach relies on the information-seeker's inferred task-related plan as the primary mechanism for suggesting how an utterance should be understood, thereby considering only interpretations that are relevant to what the information-seeker is trying to accomplish. If multiple interpretations are suggested, relevance to the current focus of attention in the dialogue and similarity to the information-seeker's actual utterance are used to select the interpretation that is most likely to represent his intended meaning or satisfy his needs.
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    Notes: In Minds, Machines, and Gödel, Lucas offers an argument, based upon Godel's incompleteness theorems, that his mind cannot be modelled by a machine. This argument has generated a variety of alleged refutations, some of which are incompatible with others. It is argued here that the incompatibility of these refutations points to a puzzle or paradox which has not yet been resolved. A solution to this puzzle is presented in which it is argued that the existence of an algorithm, capable of generating a godel sentence for an axiomatic model of that same algorithm, is not incompatible with Godels well-known results. It is further argued that, contrary to received opinion, Gödel's results do not provide grounds for believing that cognitive agents are incapable of proving the consistency of correct formal models of their own cognitive mechanisms. This is shown to be so, even on the assumption that these formal models are known by those agents (on empirical grounds) to be formal models of themselves. Finally, the implications of the above issues for theoretical questions in AI are explored.
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    Notes: Some time ago, McCarthy developed the domain circumscription formalism for closed-world reasoning. Recently, attention has been directed towards other circumscriptive formalisms. The best known of these, predicate and formula circumscription, cannot be used to produce domain-closure axioms; nor does it appear likely that the other forms can. Since these axioms are important in deductive database theory (and elsewhere), and since domain circumscription often can conjecture these axioms, there is reason to resurrect domain circumscription.
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    Notes: We study the effect of adding a rule to a rule-based heuristic classification expert system, in particular, a rule that causes an unforeseen interaction with rules already in the rule set. We show that it is possible for such an interaction to occur between sets of rules, even when no interaction is present between any pair of rules contained in these sets. A method is presented that identifies interactions between sets of rules, and an analysis is given which relates these interactions to rule-based programming practices which help to maintain die integrity of the knowledge base. We argue mat the method is practical, given some reasonable assumptions on the knowledge base.
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    Notes: This paper presents a system of nonmonotonic reasoning with defeasible rules. The advantage of such a system is that many multiple extension problems can be solved without additional explicit knowledge; ordering competing extensions can be done in a natural and defeasible way, via syntactic considerations. The objectives closely resemble Poole's objectives.But the logic is different from Poole's. The most important difference is that this system allows the kind of chaining that many other nonmonotonic systems allow. Also, the form in which the inference system is presented is quite unusual. It mimics an established system of inductive logic, and it treats defeat in the way of the epistemologist-philosophers.The contributions are both of content and of form: (content) the kinds of defeat that are considered, and (form) the way in which defeat is treated in the rules of inference.
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    Notes: This article introduces the notion of CAS-equivalent logic programs: logic programs with identical correct answer substitutions. It is shown that the notions CAS-equivalence, refutational equivalence, and logical equivalence do not coincide in the case of definite clause logic programs. Least model criteria for refutational and CAS-equivalence are suggested and their correctness is proved. The least model approach is illustrated by two proofs of CAS-equivalence. It is shown that the symmetric extension of a logic program subsumes the symmetry axiom and the symmetric homogeneous form of a logic program with equality subsumes the symmetry, transitivity, and predicate substitutivity axioms of equality. These results contribute towards the goal of building equality into standard Prolog without introducing additional inference rules.
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    Notes: This paper presents an overview and analysis of teaming in artificial neural systems (ANSs). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANSs is then described and compared with classical machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized and, where possible, the limitations inherent to specific classes of rules are outlined.
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    Notes: The paper is an annotated summary of Ehud Shapiro's report, “The Induction of Theories from Facts.” In the view of this author, Shapiro's report forms a very good foundation for work in the field of learning. It gives a clear definition of the term “learning” in a way which both is intuitively acceptable and renders learning algorithms amenable to precise analysis. It also establishes a paradigm for learning algorithms which is precise enough that it can serve as a benchmark for future development as well as for the analysis of presently available algorithms.
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    Notes: Most artificial intelligence (AI) systems are based fundamentally on deduction. Such systems usually rely on a number of deductive heuristics. We can justify this reliance only when we can assume that the logical lattice of propositions is closed. In particular, this restriction forces us to concede that deductive AI systems cannot be capable of performing activities such as abduction and inductive evaluation, which arc at the heart of scientific activity. To illustrate, we will look at diagnostic practices in medicine and compare the inductive-scientific and deductive-technological processes. Deductive AI systems can help us only with the latter type of analysis.
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    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: While significant advances have been made in real-time expert systems, it is Often a major problem to match the software with an appropriate delivery vehicle. We describe two similar approaches, developed independently and brought together in this paper, that address both the language and hardware issues. The result is an integrated solution that preserves the benefits of high-level languages for developing expert systems while meeting the constraints of many real-time applications for execution speed and data asynchronism. These constraints are met without the need for translating a high-level system into a low-level software, description. We take as a premise that expert systems technology adds value to control and data acquisition systems, and focus on the problem of engineering this technology into systems with severe resource bounds, difficult performance constraints, and non-standard hardware. Rather than taking the frontal approach of forcing predeveloped, knowledge-based code into a target machine and attempting to integrate it with a real-time component, we argue for a platform language as a basis for both the real-time and knowledge-based portions. We describe our development work and applications, which demonstrate that this strategy is practical and effective.
    Type of Medium: Electronic Resource
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  • 97
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Expert systems 4 (1987), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
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  • 98
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Expert systems 4 (1987), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: This paper describes an initial evaluation of a deep knowledge representation technique called Incremental Qualitative Analysis (IQA) to assess whether it is reasonably able to discriminate between different behaviours and hence different faults on a power plant. As an example the detection and diagnosis of faults in a PWR pressuriser sub-system have been implemented in Prolog and evaluated using transients from a reference computer model of a pressuriser in conjunction with an input processor. The application differs from earlier systems for diagnosing faults in electronic hardware in that it is necessary to represent external feedback, two-way components and sensor failures. The diagnosis is by synthesis and uses ‘constraint satisfaction and suspension’ techniques. For the present implementation all imposed faults have been successfully detected and diagnosed with reasonable determinacy, and a spurious detection has never occurred. IQA appears to be a suitable basis for knowledge-based operator aids although to produce a real-time aid appears to require more sophisticated techniques for fault detection and diagnosis, such as ‘constraint propagation’ and ‘candidate generation”. These techniques also offer the prospect of providing pseudo-causal explanations to justify their hypotheses.
    Type of Medium: Electronic Resource
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  • 99
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Expert systems 4 (1987), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract This paper describes the concepts which allow an expert system to be used for both design diagnosis and design synthesis. An example of the implementation of these concepts is presented in the domain of preliminary design of domestic kitchens in the expert system PREDIKT. PREDIKT carries out both design diagnosis and design synthesis using the same knowledge base and utilises an existing expert system shell which has forward- and backward-chaining capabilities. The significance of graphical interaction with expert systems in design domains is demonstrated.
    Type of Medium: Electronic Resource
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  • 100
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Expert systems 4 (1987), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: Knowledge acquisition is the biggest bottleneck in the development of expert systems. Fortunately, the process of translating expert knowledge to a form suitable for expert system development can benefit from methods developed by cognitive science to reveal human knowledge structures. There are two classes of these investigative methods, direct and indirect. We provide reviews, criteria for use, and literature sources for all principal methods. Direct methods discussed are: interviews, questionnaires, observation of task performance, protocol analysis, interruption analysis, closed curves, and inferential flow analysis. Indirect methods include: multidimensional scaling, hierarchical clustering, general weighted networks, ordered trees, and repertory grid analysis.
    Type of Medium: Electronic Resource
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