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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
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  • 2
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
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  • 3
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: In recent years, artificial neural networks have attracted considerable attention as candidates for novel computational systems. Computer scientists and engineers are developing neural networks as representational and computational models for problem solving: neural networks are expected to produce new solutions or alternatives to existing models. This paper demonstrates the flexibility of neural networks for modeling and solving diverse mathematical problems including Taylor series expansion, Weierstrass’s first approximation theorem, linear programming with single and multiple objectives, and fuzzy mathematical programming. Neural network representations of such mathematical problems may make it possible to overcome existing limitations, to find new solutions or alternatives to existing models, and to achieve synergistic effects through hybridization.
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  • 4
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper examines the capacity of feedforward neural networks (NNs) to approximate certain functional forms. Its purpose is to show that the theoretical property of ‘universal approximation’, which provides the basic rationale behind the NN approach, should not be interpreted too literally. The most important issue considered involves the number of hidden layers in the network. We show that for a number of interesting functional forms better generalization is possible with more than one hidden layer, despite theoretical results to the contrary. Our experiments constitute a useful set of counter-examples.
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  • 5
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: John McCardle, Neural Network Systems Techniques and Applications
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  • 6
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.
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  • 7
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: The properties of a modular neural network system designed to forecast movements of the long gilt futures contract are examnined. The examination illustrates that diagnostic measures can effectively inform end-users of the system when appropriately to switch modules given the likelihood of the degradation of forecasts. The modelling is based upon periods of market sentiment, and the inductive data models encapsulated, within the modules of the neural network system, are investigated. The models track, and also provide knowledge of , underlying market characteristics in different periods of market sentiment. The research provides deductions concerning the (dis-)similarity of underlying characteristics in differing periods of market sentiment.
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  • 8
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: The paper presents a method for helping knowledge engineers in modelling the knowledge involved in sequential diagnosis. In particular, we consider sequential diagnosis as a process which occurs in the following type of scenario: (1) there exist some candidate hypotheses which are to be pursued; (2) for each hypothesis there are some alternative tests which might be executed to pursue it; (3) the available knowledge about the world precludes projecting into the future (in other words, the available knowledge about the world does not afford the information needed for planning sequences of tests, i.e. for projecting the consequences of possible sequences of actions and picking the best sequence); (4) the choice of the next test (or tests) is made on the basis of the current state of knowledge; (5) the outcome of a test execution is affected by uncertainty. The suggested method addresses the problem of engineering the knowledge experts use for making decisions under uncertainty. A practical example of the method is also presented: at any time of the diagnostic process the expert is provided with a dynamically updated list of suggested tests in order to support him or her in the decision-making problem about which test to execute next.
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  • 9
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: The Internet offers a large potential for delivery of various information-based services, including the services of intelligent applications. As the availability of the Internet has grown, its value as a medium for the delivery of expert systems in particular has increased. There are now a large number of expert systems available on the Internet, including applications in industry, medicine, science and government. Though the Internet provides several advantages for expert system development, it also presents some special problems. These advantages and disadvantages are explored in more detail in this paper. The paper also presents a review of several Internet-based expert systems with a representative sample of publicly available applications, and a discussion of typical tools for developing Internet-based expert systems. A case study of an Internet-based expert system is presented as well.
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  • 10
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper describes a connectionist inference architecture which performs standard symbolic inference on a subclass of first-order predicate calculus. This work first involves constructing efficient connectionist mechanisms to represent basic symbol components, dynamic bindings and basic symbolic inference procedures, and devising a set of algorithms which automatically translates input descriptions to localist neural networks. These connectionist mechanisms are built by taking an existing phase-locking mechanism and extending it further to obtain desirable features to represent and manipulate basic symbol structures. The existing phase-locking mechanism represents dynamic bindings very efficiently using temporal synchronous activity between neuron elements but has fundamental limitations in supporting standard symbolic inference. The extension addresses these limitations. The extended system encodes a significant subset of a Horn clause language in a connectionist style.
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  • 11
    Electronic Resource
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Connectionist network learning of context-free languages has so far been applied only to very simple cases and has often made use of an external stack. Learning complex context-free languages with a homogeneous neural mechanism looks like a much harder problem. The current paper takes a step toward solving this problem by analyzing context-free grammar computation (without addressing learning) in a class of analog computers called dynamical automata, which are naturally implemented in connectionist networks. The result is a widely applicable method of using fractal sets to organize infinite-state computations in a bounded state space. An appealing consequence is the development of parameter-space maps, which locate various complex computers in spatial relationships to one another. An example suggests that such a global perspective on the organization of the parameter space may be helpful for solving the hard problem of getting connectionist networks to learn complex grammars from examples.
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  • 12
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Holographic reduced representations (HRRs) are a method for encoding nested relational structures in fixed-width vector representations. HRRs encode relational structures as vector representations in such a way that the superficial similarity of the vectors reflects both superficial and structural similarity of the relational structures. HRRs also support a number of operations that could be very useful in psychological models of human analogy processing: fast estimation of superficial and structural similarity via a vector dot-product; finding corresponding objects in two structures; and chunking of vector representations. Although similarity assessment and discovery of corresponding objects both theoretically take exponential time to perform fully and accurately, with HRRs one can obtain approximate solutions in constant time. The accuracy of these operations with HRRs mirrors patterns of human performance on analog retrieval and processing tasks.
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  • 13
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
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  • 14
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
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  • 15
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
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  • 16
    Electronic Resource
    Electronic Resource
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper addresses the topic of a predictive task after integration of symbolic and numerical features by a hybrid diagnostic expert system. The online interaction of information and knowledge from various sources is achieved after successful combination of different development environments, tools and programs. The system infers using cooperatively dynamic modelling information, online sensor information, and stored knowledge in the knowledge base.
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  • 17
    Electronic Resource
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.
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  • 18
    Electronic Resource
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Knowledge-based modeling and implementation of the various manufacturing processes represent an intensive research area. It is known that it is difficult to analyze the mechanisms of many industrial production processes and build dynamic models by employing classical methods for intelligent systems in manufacturing. This paper describes how to use dynamic recurrent neural networks to provide the model base of a hybrid intelligent system for the metallurgical industry with a quality control model. The hybrid system extracts the features of image sequences obtained through the vision detection subsystem and employs a dynamic recurrent neural network to assess and predict the product qualities to further coordinate the entire production process.
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  • 19
    Electronic Resource
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: An architectural configuration of a knowledge-based system for production rescheduling reported in this paper uncovers a number of points of interest to practitioners as well as researchers. The study shows that knowledge-based methods applied to production rescheduling are a valuable approach for manufacturers to manage production disturbances and deliver customer orders on time. Very often, developing an effective scheduling system whilst solving some problems requires an appropriate combination of a rigorous analysis of the production system state and the rules of thumb used by the human scheduler. In the actual performance of this hybrid system, an expert simulation system was used to produce new schedules that fit the real production environment.
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  • 20
    Electronic Resource
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
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  • 21
    Electronic Resource
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Expert systems are an evolving technology with the potential to make human expertise widely and cheaply available. The literature describing the development of expert systems generally assumes that experts willingly give up their knowledge. This is unrealistic and may be a reason why most expert system projects fail. This paper explores the problem of unwilling experts from the perspective of a knowledge engineer building an expert system. The link between knowledge and organizational power is established and human motivation theories are discussed. Finally, a new motivational approach is introduced to help the knowledge engineer deal with unwilling experts.
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  • 22
    Electronic Resource
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Knowledge sorting is one way to organize the knowledge acquired from the domain expert(s) and various sources. Well-organized knowledge representation will make rule extraction much easier. This paper describes a knowledge sorting process that was developed to facilitate the rule extraction for a product design expert system. The process capitalizes on the relationships between design attributes and factors, dependent and independent variables, and consists of three stages: identification of knowledge sources, generation of taxonomic trees, and organization of acquired knowledge. An example applies the sorting process to the development of an expert system for the design of wood head golf clubs.
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  • 23
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper represents a personal perspective on the last 13 years of research in medical artificial intelligence and expert systems, and describes the progress of the discipline from its inception to its current status. The challenges faced by researchers are examined, and it is argued that (despite the claims) theoretical and technical limitations are not the major barriers to the successful implementation of medical artificial intelligence and expert systems, but rather more complex professional and organizational issues are at stake.
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  • 24
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper presents the knowledge organization for a simulation subsystem that is a component of a comprehensive expert system for failure modes and effects analysis. Organizing the simulation subsystem’s knowledge base around a function-centered ontology produces an architecture that facilitates reasoning about an engineering design at multiple levels of abstraction and throughout the life-cycle of the design. Moreover, the resulting architecture provides the capability for incorporating computer-aided analysis and design tools early on into the conceptual design of an engineering system before a commitment is made to a specific technology to implement the system’s behavior. The result is an expert system simulation knowledge source that can be used to reason about the effects of system failures based on conceptual designs, i.e. designs in which commitments to an underlying technology to achieve the system’s function have not yet been made but computer-aided assistance for reasoning about the system’s potential failure modes and effects is useful.
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  • 25
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Variable binding has long been a challenge to connectionists. Attempts to perform variable binding using localist and distributed connectionist representations are discussed, and problems inherent in each type of representation are outlined.
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  • 26
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Books reviewed:Daniel Kustrin, Neural Network Analysis, Architectures and ApplicationsSimon O’Keefe, Fuzzy and Neural Approaches in EngineeringJohn McCardle, Neural Network Systems Techniques and Application
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  • 27
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
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  • 28
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
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  • 29
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
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  • 30
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
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  • 31
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
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  • 32
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Applications using expert systems for monitoring and control problems often require the ability to represent temporal knowledge and to apply reasoning based on that knowledge. Incorporating temporal representation and reasoning into expert systems leads to two problems in development: dealing with an implied temporal order of events using a non-procedural tool; and maintaining the large number of temporal relations that can occur among facts in the knowledge base. In this paper we explore these problems by using an expert system shell, CLIPS (C Language Integrated Production System), to create temporal relations using common knowledge-based constructs. We also build an extension to CLIPS through a user-defined function which generates the temporal relations from those facts. We use the extension to create and maintain temporal relations in a workflow application that monitors and controls an engineering design change review process. We also propose a solution to ensure truth maintenance among temporally related facts that links our temporal extension to the CLIPS facility for truth maintenance.
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  • 33
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
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  • 34
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    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Push technology is the term recently linked with Web-based products to indicate the built-in capability of the software package to deliver relevant information to the user automatically. In this paper, a fuzzy push delivery scheme is described which ‘observes’ the movements of the user within the Web pages and then evaluates the personal interests and preferences of the user. With the information available from the fuzzy scheme, Web site information favoured by visitors can be ‘pushed’ to them without even being requested. To verify the feasibility of the concept, a Web site with various shopping spots has been developed with built-in functions to detect the time of stay in various sites by visitors . Output from the proposed fuzzy scheme is compared with the perception of the interest level given by the visitors themselves for its evaluation. The built-in feature of the scheme can significantly enhance the functionality and attractiveness of the Web site involved and hence improve its competitiveness in the global market. The development of the proposed scheme and its evaluation in an emulated Internet environment are covered in the paper.
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  • 35
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    Expert systems 16 (1999), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Fault diagnosis has become increasingly important for industrial automation and many approaches have been investigated for the online diagnostic task. This work demonstrates a new online expert system for dynamic industrial automated processes. The emphasis of this diagnostic system lies in the functions provided for fault detection, prediction, compensation and diagnosis. The system uses experiential knowledge cooperatively with scientific knowledge in a new interactive formation. It is developed in two parts using the DASYLab software for the numerical computations and the KPWin development tool for the symbolic representations. The system was developed in collaboration with the company Automation Systems S.A., which specializes in hydraulic systems, and is used for fault detection in production machines.
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  • 36
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    Expert systems 16 (1999), S. 0 
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    Topics: Computer Science
    Notes: In general, expert system applications to real cases involve making decisions, i.e. selecting a suitable action among a set of possible alternative actions. A well-known standard method for modelling decision problems is the so-called multi-attribute utility theory (MAUT), a method in which the alternatives are viewed in terms of their attributes. A set of attributes are identified and a specific value and a suitable relative importance weight are assigned to each attribute. However, it is not easy for the expert to quantify the relative importance weight of an attribute: this assignment entails a certain abstraction activity from the expert and, as is well known, experts have difficulty in providing their knowledge in abstract and general terms. In order to overcome this difficulty we propose a method for automatically inferring relative importance weights from a set of specific action sequences. An action sequence is a list in chronological order of the actions executed by the expert when facing specific cases of decision problems. Providing action sequences requires no other effort but remembering specific episodes, and this task is much easier for experts than having to directly provide precise numbers expressing relative importance weights. Moreover in many cases action sequences are already stored in suitable records. Consider, for example, the list of medical tests executed on a given patient, a list included in the patient clinical record stored in the clinical database of a hospital. On the basis of these considerations the proposed method should be useful for designers of expert systems which face problems of choosing the right action among a set of alternative actions.
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  • 39
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    Expert systems 17 (2000), S. 0 
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    Expert systems 17 (2000), S. 0 
    ISSN: 1468-0394
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    Topics: Computer Science
    Notes: Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for use in expert systems and to the traditional statistical task of classification. The method is based on a novel classification metric, implemented as upper and lower approximations of a set and more generally in terms of positive, negative and boundary regions. Classification accuracy, which may be set by the decision maker, is measured in terms of conditional probabilities for equivalence classes, and the method involves a search for subsets of attributes (called ’reducts’) which do not require a loss of classification quality. To illustrate the technique, RST is employed within a state level comparison of education expenditure in the USA.
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    Expert systems 17 (2000), S. 0 
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    Notes: The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the ’self-momentum’, ’Freud’ and ’Jung’ rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated ’learning to learn’ characteristics are now under study and test.
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    Notes: This paper describes the design and implementation of an intelligent real-time supervision system for the monitoring and control of wastewater treatment plants. The system presents a hybrid structure which integrates knowledge engineering techniques and methodologies (an expert system based on production rules embodying non-mathematical or semiquantitative knowledge of unit operation) and algorithmic process-related methods (data acquisition, signal preprocessing, calculation of plant operation parameters etc.). The fundamental design aspects consist of temporal information treatment, artifact detection and a client- server structure which allows the telesupervision of various plants by a central system.
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    Notes: Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), human experts rarely use a single type of knowledge to solve a real-world problem. A human expert usually combines a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in the intelligent systems area. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid epidemic screening KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed. The system has been tested using real epidemic screening variables and data.
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    Notes: While knowledge-based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in-depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain-dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal-driven knowledge acquisition tool (GDKAT) that helps elicit and store experts’ declarative and procedural knowledge in knowledge bases for a user-defined domain. The designed tool is implemented using the object-oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.
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    Notes: In this paper we propose a connectionist model for variable binding. The model is topology dependent on the graph it builds based on the predicates available. The irregular connections between perceptron-like assemblies facilitate forward and backward chaining. The model treats the symbolic data as a sequence and represents the training set as a partially connected network using basic set and graph theory to form the internal representation. Inference is achieved by opportunistic reasoning via the bidirectional connections. Consequently, such activity stabilizes to a multigraph. This multigraph is composed of isomorphic subgraphs which all represent solutions to the query made. Such a model has a number of advantages over other methods in that irrelevant connections are avoided by superimposing positionally dependent sub-structures that are identical, variable binding can be encoded and multiple solutions can be extracted simultaneously. The model also has the ability to adapt its existing architecture when presented with new clauses and therefore add new relationships/rules to the model explicitly; this is done by some partial retraining of the network due to the superimposition properties.
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    Notes: Modus ponens is used in forward inference and backward inference, where the truth of the conclusion is inferred from the truth of the premise. In modus tollens, the falseness of the premise is inferred from the falseness of the conclusion. Although modus ponens is used in general connectionist production systems, modus tollens is rarely used, except in Quinlan’s proposed INFERNO system and in the system proposed by Thornber. A connectionist production system called ConnPS that can perform both modus ponens and modus tollens simultaneously is described. Compared to the INFERNO system, one of the advantages of ConnPS is its supervised learning ability. The rules and examples given as external knowledge are often erroneous and incomplete. In ConnPS, these rules can be refined by using the supervised learning. Both positive and negative examples are presented to ConnPS, onto which the external rules and observations are mapped. Moreover, ConnPS’s implementations of implications, conjunctions, disjunctions and negation are intuitively consistent with Boolean logic.
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    Expert systems 16 (1999), S. 0 
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    Notes: Knowledge-based modeling and implementation of the various urban planning processes represent an intensive research area. This paper presents a hybrid artificial intelligence system using a knowledge-based approach, neural networks and fuzzy logic that automates the decision-making process in urban planning. The system is used for developing urban development alternatives based on real-world data. Results show that, by integrating knowledge-based systems, artificial neural networks and fuzzy systems, the system achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application. With this approach, the best of three technologies can be compiled together to solve complex urban problems. We discuss the structure of the combined technologies, as well as providing examples of its application in the field of urban development.
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    Expert systems 16 (1999), S. 0 
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    Notes: This paper describes the development of a system, ICADS (Intelligent Car Audio Design System), that can be used to effectively support product development engineers in car audio product design. We have demonstrated the use of expert system technology and the technique of quality function deployment (QFD) in supporting car audio design planning. QFD is applied as a knowledge acquisition method in this study to support car audio design teams in the development of products in a structured way that relates market demand via engineering specifications to parts specifications. We believe that other industries can use a similar approach for developing an expert system that can support product design planning.
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    Notes: Our research originates from a study of the possibilities of integrating rules and objects in knowledge-based systems. In the present work, we are interested in the interactionist perspective of an object. The stepwise reasoning of a diagnostic expert system, possibly involving subgoaling and interactions with the environment, can be easily codified by means of production rules over proposition literals. This set of rules can be graphically represented in a network manner denoting the relations between the rules. The individual nodes in the network can be expressed by means of autonomous objects and their relations, interpreted as possible communications between them. The objects are given a structure and a proper behaviour and cooperate for performing logical reasoning by means of forward and backward chaining inference processes. Therefore, designing this system implies addressing several basic issues such as inter-object communications and their synchronization. The problem here is not necessarily to develop a great intelligence locally but to develop strong networks of good communicators. This approach belongs to the interactionist representation current, where objects are called actors. In principle, the actors may carry out computation in parallel and provide a conceptual foundation for massively concurrent object-oriented paradigms. From this point of view, a system allowing for the simultaneous investigation of several rules and premises in the forward or the backward chaining would be significantly more efficient.
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    Mathematical finance 10 (2000), S. 0 
    ISSN: 1467-9965
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    Topics: Mathematics , Economics
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    Expert systems 15 (1998), S. 0 
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    Notes: Abstract: Berthing ships at a port and unloading the stowed materials require a series of scheduling problems: ship berthing, discharging, and material transport. To deal effectively with the scheduling complexity due to mutual interdependence among those problems, this paper proposes a two-level hierarchical architecture for the integrated scheduling of all the activities arising in port. The hierarchical architecture assigns ships to multiple lower level dispatchers, each of which makes its own discharging and material transport schedule independently while satisfying the requirements imposed by the higher level coordinator. If any problem occurs within a dispatcher, the higher level coordinator attempts to resolve the trouble through the coordination of other dispatchers. Based on the hierarchical architecture, a prototype scheduling expert system is developed using G2 for the port scheduling problem at a steelworks. Through the object- oriented interaction of frames, the system is shown to effectively construct integrated schedules from the berth scheduling to the material transport scheduling.
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    Notes: Abstract: Creating and sustaining a competitive advantage in today’s turbulent world markets is a formidable business challenge. For many organisations, operating within an inherently unstable external environment has resulted in the need for a cultural rethink. Business complacency, brought about by corporate industry monopolisation, the reliance on reputation, brand identity, and customer allegiance, is no longer a recipe for sustained market growth. Companies have realised that being market focused could provide them with a competitive edge within the marketplace. To achieve this organisations have turned to the ‘Quality movement’, embracing the Total Quality Management (TQM) philosophy and its inherent tools (auditing frameworks, concept of quality systems etc.) as a means to recapture lost ground. ESAS (Enterprise Strategic Advisory System) is a prototype intelligent system that utilises a case- based approach to address the issues raised above, by providing organisations with advice on market- led quality and competitive strategy. This paper explores the philosophy and ideas behind the ESAS concept, and describes the development of an ESAS prototype.
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    Notes: Gillies, Donald, Artificial Intelligence and Scientific MethodGurney, Kevin, An Introduction to Neural Networks
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    Expert systems 15 (1998), S. 0 
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    Notes: Computer-based training is an area where knowledge-based techniques enable us to achieve new system functionalities which cannot be attained with conventional techniques. Knowledge-based training systems can adapt the course, the lesson and the instructional strategy to the needs and attitude of the individual trainee, so as to obtain a completely individualised instruction. Due to these capabilities they are known as Intelligent Training Systems (ITSs). After their introduction in the mid-eighties with their first successful application in the military sector, ITSs are now mature enough for practical use in civil applications.This paper presents the experience gained by the authors over the last decade through the realisation of two industrial prototypes in different sectors and the recent development of a full-scale application. Such experience led to a specification methodology and to a development framework which now provide a solid baseline for the industrial application of the ITS technology.
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    Notes: Constraint satisfaction and search problems generally fall into the class of problems for which a direct algorithmic solution does not exist. The solution of these problems requires the examination of state spaces. A problem solver (inference engine) alone is not able to organize and maintain state space consistently. For this purpose a so-called truth maintenance system is required. Our truth maintenance system (MEKON) organizes data within a data abstraction called a context. Each context corresponds to one problem state and contains currently believed data. The truth maintenance system provides believed data retrieval, belief revision, contradiction handling and non-monotonicity handling, the features that help a problem solver to examine state spaces. MEKON represents an ATMS-like system implemented within BEST (Blackboard-based Expert System Toolkit). However, some special MEKON features such as state variables, context sensitive generation of assumptions and explicit context generation, that are not present in standard ATMSs, facilitate not only the solution of constraint satisfaction problems, but also the solution of search problems (not provided by standard ATMSs). Being deeply integrated with Prolog/Rex, BEST’s knowledge representation language, and BEST’s inference engine, MEKON provides a simple and efficient means for the examination of state spaces. Facts, hypotheses (assumptions), and concepts (frames) are used to describe a problem state, contexts are used to represent points in the state spaces, while rules are used to perform state transitions. MEKON is the only truth maintenacne system that provides truth maintenance capabilities on local blackboards thus enabling the solution of complex problems which include different kinds of real constraint satisfaction and search problems concerning diagnosis, allocation tasks, classification tasks, planning or scenario making.
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    Expert systems 14 (1997), S. 0 
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    Notes: Russell, Stuart and Norvig, Peter Artificial Intelligence: A Modern ApproachEdwards, Alistair, D.N. (ed.) Extra-Ordinary Human-Computer Interaction: Interfaces for Users with DisabilitiesVacca, John R. VRML: Bringing Virtual Reality to the Internet
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    Notes: The blackboard architecture, originally developed for the system that permits the comprehension of language, HEARSAY II, has later been used in a great variety of domains and in various environments for the construction of systems. From the classic architecture of HEARSAY II, many applications, generalizations, extensions and refinements have been developed. In this paper we present REDSIEX, (RED de SIstemas EXpertos) which is a network of expert systems within a blackboard architecture, for the cooperation solution of distributed problems. The REDSIEX system inherits various of the elements defined by the architecture of HEARSAY II and incorporates new components and organization. These produce a very characteristic and exclusive global work style in the solution of problems, within a conceptual framework of emergent control. The main structural and functional characteristics of REDSIEX are discussed.
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    Notes: This paper presents an intelligent system to assist small investors to determine stock trend signals for investment in stock business. A pilot system is built providing three main categories of technical and analysis theories, namely momentum, moving average, and support/resistance line. It has extensive graphic interface design to facilitate the usage of the system. For novice investors, the system is associated with tutoring features and it supports analysis study of the rationale behind some system recommendations. Skilful investors can explore the various theories for the prediction by means of adjusting the weightings, combinations and even some independent variables allocated by the intelligent system. General users can therefore formulate their investment strategies upon system recommendations under different investment criteria accordingly.
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    Notes: The manner in which CARMEN (Constraints And Rules Management ENgine), an operational expert system generator developed as part of the ESPRIT project ITHACA, models and acquires knowledge is discussed. Central to CARMEN are three types of control entities (TASKs, MKSs and BKSs) which are used to describe problem-solving at the knowledge-use level. Modeling of domain knowledge is done both at the deep and surface levels. The Integrity Checking Task (or ICT) is used to illustrate the manner in which CARMEN works. We conclude the paper with a comparison of CARMEN with other approaches in knowledge modeling.
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    Notes: The usefulness of ‘user centred design’ as a concept in the design of software systems is considered. It is concluded that the concept is frequently used in a way so vacuous as to be devoid of meaning, and would be better discarded. Some suggestions are made for further research in this area; some modest suggestions are also offered to those who wish to develop systems of a type better suited to the needs of civilized society.
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    Notes: Increasing evidence over the past decade indicates that financial markets exhibit nonlinear dynamics in the form of chaotic behavior. Traditionally, the prediction of stock markets has relied on statistical methods including multivariate statistical methods, autoregressive integrated moving average models and autoregressive conditional heteroskedasticity models. In recent years, neural networks and other knowledge techniques have been applied extensively to the task of predicting financial variables.This paper examines the relationship between chaotic models and learning techniques. In particular, chaotic analysis indicates the upper limits of predictability for a time series. The learning techniques involve neural networks and case–based reasoning. The chaotic models take the form of R/S analysis to measure persistence in a time series, the correlation dimension to encapsulate system complexity, and Lyapunov exponents to indicate predictive horizons. The concepts are illustrated in the context of a major emerging market, namely the Polish stock market.
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    Notes: This paper concerns the study of destination choice modelling, more specifically identifying within some area (e.g. a city) the region where a particular store is the most favourable to be visited by individuals. An influence measure is constructed for each individual, which incorporates the modern technique known as Dempster–Shafer theory. Based on the evidence of the shopping destinations of individuals, geographical regions are found for levels of largest belief and plausibility (within Dempster–Shafer theory) for specific stores being the most favourable to visit. Additionally, this method may be used to identify the possible position of new stores, based on regions of most uncertainty or conflict in store choice. A prototype choice modelling system is introduced to enable the series of associated results to be easily visualized and analysed.
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    Notes: Fuzzy logic principles and neural networks, both being computational intelligence technologies, can be combined to produce synergetic effects through the formation of a unified approach which takes advantage of the benefits and at the same time counterbalances the flaws of the two technologies. In this paper, a fuzzy neural approach, which is characterized by its ability to suggest the appropriate adjustment of product quantity from various suppliers with different quality standards in a supply chain network, is presented. This approach is particularly useful in situations where multiple supply chain partners are involved to achieve the common objective of producing products to the best satisfaction of customer demands at the lowest possible cost. To validate the feasibility of this approach, a test has been conducted based on the proposed fuzzy neural approach with the objective of suggesting the appropriate selection of suppliers and the optimal quantity allocated to them to meet the required quality standards. This paper describes the methodology for the deployment of this proposed hybrid approach to enhance the machine intelligence of a supply chain network with the description of a case study to exemplify its underlying principles.
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    Expert systems 18 (2001), S. 0 
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    Notes: Recent advances related to on-line analytical processing (OLAP) have resulted in a significant improvement in data analysis efficiency by virtue of its multidimensional database structure and pre-computing operations of measuring data. However, the research related to the design and implementation of OLAP, particularly in the support of dispersed manufacturing networks in terms of ‘intelligent decision making’, has yet to be considered as remarkable. Research studies indicate that the level of intelligence of decision support systems can be enhanced with the incorporation of computational intelligence techniques such as case-based reasoning or rule-based reasoning. This paper describes the development of an intelligent data-mining system using a rule-based OLAP approach which can be adopted to support dispersed manufacturing networks in terms of performance enhancement. In this paper, the techniques, methods and infrastructure for the development of such a data-mining system, which possesses certain intelligent features, are presented. To validate the feasibility of this approach, a case example related to the testing of the approach in an emulated industrial environment is covered.
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    Notes: In this paper, an expert system for automobile air compressor troubleshooting (ACTS) is presented. This system can assist users to conduct an efficient and effective diagnosis on air compressor failures. Unlike most diagnosis expert systems, ACTS uses a new control strategy to enhance the efficiency of the diagnostic process. This control strategy attempts to spend the least amount of time to detect the compressor fault accurately by investigating only portions of the knowledge base. ACTS first constructs a diagnostic tree based on the functions or connectivity of the air compressor's devices. A fuzzy multiple-attribute decision-making method is used to determine the priority of the nodes (devices) in the diagnostic tree. The prioritized result creates a ‘meta knowledge base’ to control the diagnostic process. In addition, each node possesses its own knowledge base for hypothesizing the possible faults for the node. ACTS, written in MS Visual BASIC, has been successfully developed and implemented in MS-Windows environment on a PC. To validate the system performance, ACTS is compared to EXACT, an expert system for automobile air compressor troubleshooting, using 50 sample cases. The evaluation results indicate that ACTS performs better than EXACT by reducing the number of queries and the diagnosis time by 20.7% and 24.9%, respectively.
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    Notes: The development of genetic algorithms started almost three decades ago in an attempt to imitate the mechanics of natural systems. Since their inception, they have been applied successfully as optimization methods, and as expert systems, in many diverse applications. In this paper, a genetic-algorithm-based expert system shell is presented that, when combined with a proper database comprising the available energy-saving technologies for the process industry, is able to perform the following tasks: (a) identify the best available technologies (BATs) among the available ones for a given process industry, and (b) calculate their optimal design parameters in such a way that they comply with the energy requirements of the process. By the term BAT is meant the available energy-saving technology, among the existing ones in the market, that is the best for the case.
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    Expert systems 18 (2001), S. 0 
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Fault diagnosis is very important for modern production technology and has received increasing theoretical and practical attention during the last few years. This paper presents a model-based diagnostic method for industrial systems. An online, real-time, deep knowledge based fault detection system has been developed by combining different development environments and tools. The system diagnoses, predicts and compensates faults by coupling symbolic and numerical data in a new environment suitable for the interaction of different sources of knowledge and has been successfully implemented and tested on a real hydraulic system.
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    Topics: Computer Science
    Notes: We have developed a bilingual interface to the Novell network operating system, called the Dialoguer. This system carries on a conversation with the user in Arabic or English or a combination of the two and attempts to help the user use the Novell network operating system. Learning to use an operating system is a major barrier in starting to use computers. There is no single standard for operating systems which makes it difficult for novice users to learn a new operating system. With the proliferation of client–server environments, users will eventually end up using one network operating system or another. These problems motivated our choice of an area to work in and they have made it easy to find real users to test our system.This system is both an expert system and a natural language interface. The system embodies expert knowledge of the operating system commands and of a large variety of plans that the user may want to carry out. The system also contains a natural language understanding component and a response generation component. The Dialoguer makes extensive use of case frame tables in both components. Algorithms for handling a bilingual dialogue are one of the important contributions of this paper along with the Arabic case frames.
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    Expert systems 18 (2001), S. 0 
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    Topics: Computer Science
    Notes: This paper describes the implementation of evolutionary techniques for information filtering and collection from the World Wide Web. We consider the problem of building intelligent agents to facilitate a person's search for information on the Web. An intelligent agent has been developed that uses a metagenetic algorithm in order to collect and recommend Web pages that will be interesting to the user. The user's feedback on the agent's recommendations drives the learning process to adapt the user's profile with his/her interests. The software agent utilizes the metagenetic algorithm to explore the search space of user interests. Experimental results are presented in order to demonstrate the suitability of the metagenetic algorithm's approach on the Web.
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  • 83
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    Topics: Computer Science
    Notes: Although developments on software agents have led to useful applications in automation of routine tasks such as electronic mail filtering, there is a scarcity of research that empirically evaluates the performance of a software agent versus that of a human reasoner, whose problem-solving capabilities the agent embodies. In the context of a game of a chance, namely Yahtzee©, we identified strategies deployed by expert human reasoners and developed a decision tree for agent development. This paper describes the computer implementation of the Yahtzee game as well as the software agent. It also presents a comparison of the performance of humans versus an automated agent. Results indicate that, in this context, the software agent embodies human expertise at a high level of fidelity.
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    Expert systems 18 (2001), S. 0 
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    Topics: Computer Science
    Notes: The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the ‘knowledge acquisition’ problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the ‘paradox of expertise’, the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.
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    Topics: Computer Science
    Notes: As more intelligent systems are introduced into the marketplace, it is becoming increasingly urgent to consider usability for such systems. Historically, the two fields of artificial intelligence (AI) and human- computer interaction (HCI) have had little in common. In this paper, we consider how established HCI techniques can usefully be applied to the design and evaluation of intelligent systems, and where there is an urgent need for new approaches. Some techniques - notably those for requirements acquisition and empirical evaluation - can usefully be adopted, and indeed are, within many projects. However, many of the tools and techniques developed within HCI to support design and theory-based evaluation cannot be applied in their present forms to intelligent systems because they are based on inappropriate assumptions; there is consequently a need for new approaches. Conversely, there are approaches that have been developed within AI - e.g. in research on dialogue and on ontologies - that could usefully be adapted and encapsulated to respond to this need. These should form the core of a future research agenda for intelligent interaction design.
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    Expert systems 18 (2001), S. 0 
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    Topics: Computer Science
    Notes: This paper examines a neural network method known as the self-organizing map (SOM). The motivation behind the SOM is to transform the data to a two-dimensional grid of nodes while preserving its ’topological’ structure. In neural network terminology this involves unsupervised learning. The nearest related statistical technique is cluster analysis. We employ the SOM in the task of identifying strategic groups of companies, using data which relate to the generic strategies suggested by Porter. Following identification of different groups of hotels with certain strategic emphases, the study investigates correlations between strategies followed and hotel performance. We compare and contrast the ’feature map’ generated by the SOM with the results of a standard cluster analysis using the k-means method. The data also cover performance indicators and the results indicate that performance varies between strategic groups.
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    Expert systems 18 (2001), S. 0 
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    Topics: Computer Science
    Notes: Product development stages. The analytic hierarchy process (AHP), which breaks down a complex problem into simple hierarchical decision-making processes, can be incorporated with fuzzy logic to suggest the relative strength of the factors in the corresponding criteria, thereby enabling the construction of a fuzzy judgement matrix to facilitate decision-making. This paper proposes a fuzzy AHP approach in modular product design complemented with a case example to validate its feasibility in a real company. Test findings indicate that the approach is helpful for providing key decision support information in terms of product module selection during product development stages. The significance of the contribution of this paper is the suggestion of a novel approach in modular product design, embracing a combination of computational intelligence and traditional techniques, thereby providing more alternatives and ideas for those researchers who are interested in this field of study.
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    Topics: Computer Science
    Notes: Book reviewed: The Essence of Expert Systems, Susan J. Chinn
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    Expert systems 17 (2000), S. 0 
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    Topics: Computer Science
    Notes: This paper focuses on the knowledge representation framework utilized by an integrated wastewater treatment expert system for diagnosing operational problems of an activated sludge plant. The system deals with events that may occur in all the units of an activated sludge plant and exploits on-line measurements, observations formed from data provided by laboratory analyses and empirical observations in an integrated manner. The system provides assistance to human experts to control the activated sludge process. It has been tested and evaluated in the pilot activated sludge plant of the Water and Air Quality Laboratory in the University of the Aegean.
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    Computational intelligence 16 (2000), S. 0 
    ISSN: 1467-8640
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    Computational intelligence 16 (2000), S. 0 
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    Topics: Computer Science
    Notes: We discuss the use of probability-based natural language processing for Chinese text retrieval. We focus on comparing different text extraction methods and probabilistic weighting methods. Several document processing methods and probabilistic weighting functions are presented. A number of experiments have been conducted on large standard text collections. We present the experimental results that compare a word-based text processing method with a character-based method. The experimental results also compare a number of term-weighting functions including both single-unit weighting and compound-unit weighting functions.
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    Computational intelligence 16 (2000), S. 0 
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    Topics: Computer Science
    Notes: Recently, the prevalence of information retrieval engines has created an important application of the automatic summarization as the display of retrieval results, whereby the user can quickly and accurately judge the relevance of texts returned as a result of a query. Here, rather than producing a generic summary, the summary that reflects the user's topic of interest (information need) expressed in the query would be considered as more suitable. This type of summary is often called query-biased summary.In this paper we present a method for producing query-biased summaries using lexical chains. Lexical chains are sequences of words that are in lexical cohesion relation with each other, and tend to indicate fragments of a text that form a semantic unit. Using lexical chains would enable to produce more coherent and readable summaries than previous approaches to query-biased summarization.To evaluate the effectiveness of our method, a task-based evaluation scheme is adopted. The results from the experiments show that query-biased summaries by lexical chains outperform others in the accuracy of subjects' relevance judgments.
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    Computational intelligence 16 (2000), S. 0 
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    Topics: Computer Science
    Notes: Time-series prediction is important in physical and financial domains. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This paper describes a Pattern Modelling and Recognition System (PMRS) which is used for forecasting benchmark series and the US S&P financial index. The main aim of this paper is to evaluate the performance of such a system on noise free and Gaussian additive noise injected time-series. The results show that the addition of Gaussian noise leads to better forecasts. The results also show that the Gaussian noise standard deviation has an important effect on the PMRS performance. PMRS results are compared with the popular Exponential Smoothing method.
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    Mathematical finance 11 (2001), S. 0 
    ISSN: 1467-9965
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Mathematics , Economics
    Notes: In frictionless securities markets, the characterization of the no-arbitrage condition by the existence of equivalent martingale measures in discrete time is known as the fundamental theorem of asset pricing. In the presence of convex constraints on the trading strategies, we extend this theorem under a closedness condition and a nondegeneracy assumption. We then provide connections with the superreplication problem solved in Föllmer and Kramkov (1997).
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    Mathematical finance 11 (2001), S. 0 
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    Topics: Mathematics , Economics
    Notes: Working in a binomial framework, Boyle and Vorst (1992) derived self-financing strategies perfectly replicating the final payoffs to long positions in European call and put options, assuming proportional transactions costs on trades in the stocks. The initial cost of such a strategy yields, by an arbitrage argument, an upper bound for the option price. A lower bound for the option price is obtained by replicating a short position. However, for short positions, Boyle and Vorst had to impose three additional conditions. Our aim in this paper is to remove Boyle and Vorst's conditions for the replication of short calls and puts.
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    Mathematical finance 10 (2000), S. 0 
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    Topics: Mathematics , Economics
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