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  • 101
    Electronic Resource
    Electronic Resource
    News (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00138
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  • 102
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    Classification and rule induction using rough set theory (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00136
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  • 103
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    Feedforward networks in financial predictions: the future that modifies the present (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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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    URL: http://dx.doi.org/10.1111/1468-0394.00137
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  • 104
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    Diary (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00139
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  • 105
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    Knowledge-based system for telecontrol of anaerobic wastewater treatment plants (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00129
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  • 106
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    A hybrid system applied to epidemic screening (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
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    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), 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00130
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  • 107
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    GDKAT: A goal-driven knowledge acquisition tool for knowledge base development (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00131
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  • 108
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    A connectionist inference model for pattern-directed knowledge representation (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00132
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  • 109
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    News (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00133
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  • 110
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    Diary (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00134
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  • 111
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    Editorial: Symbol processing in connectionist systems (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00122
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  • 112
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    A connectionist production system which can perform both modus ponens and modus tollens simultaneously (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
    Details
    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00123
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  • 113
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    Hybrid artificial intelligence approach to urban planning (1999)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00117
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  • 114
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    ICADS: Intelligent Car Audio Design System for product planning (1999)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00089
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  • 115
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    An actor model for rule-based systems (1999)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 16 (1999), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00088
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  • 116
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    A hierarchical expert system for integrated scheduling of ship berthing, discharging and material transport (1998)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 15 (1998), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00082
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  • 117
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    ESAS: A case-based approach to business planning and monitoring (1998)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 15 (1998), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00076
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  • 118
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    The Two Faces of AI: AAAI ’97 - Providence, Rhode Island, USA, 27–31 July 1997 (1998)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 15 (1998), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00070
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  • 119
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    Book Reviews (1998)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 15 (1998), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Gillies, Donald, Artificial Intelligence and Scientific MethodGurney, Kevin, An Introduction to Neural Networks
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00063
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  • 120
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    Towards Industrial Application of Intelligent Training Systems (1998)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 15 (1998), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00060
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  • 121
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    Truth Maintenance in Blackboard Environment (1998)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 15 (1998), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00061
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  • 122
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    Book Reviews (1997)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 14 (1997), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00057
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  • 123
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    REDSIEX: A Cooperative Network of Expert Systems with Blackboard Architectures (1997)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 14 (1997), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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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    URL: http://dx.doi.org/10.1111/1468-0394.00055
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  • 124
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    Diary (1997)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 14 (1997), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.t01-3-00058
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  • 125
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    An Intelligent Business Advisor System for Stock Investment (1997)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 14 (1997), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00049
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  • 126
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    Using the CARMEN’s framework to model and acquire knowledge (1997)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 14 (1997), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/1468-0394.00043
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  • 127
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    The analysis and performance evaluation of the pheromone-Q-learning algorithm (2004)
    Oxford, UK : Blackwell Publishing
    Expert systems 21 (2004), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone.
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    The application of a fuzzy-rule-based system in an exposition of the antecedents of sedge warbler song flight (2004)
    Oxford, UK and Boston, USA : Blackwell Publishing Ltd
    Expert systems 21 (2004), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract:  The utilization of a fuzzy aspect within data analysis attempts to move from a quantitative to a more qualitative investigative environment. As such, this may allow the more non-quantitative researchers results they can use, based on sets of linguistic terms. In this paper an inductive fuzzy decision tree approach is utilized to construct a fuzzy-rule-based system for the first time in a biological setting. The specific biological problem considered attempts to identify the antecedents (conditions in the fuzzy decision rules) which characterize the length of song flight of the male sedge warbler when attempting to attract a mate. Hence, for a non-quantitative investigator the resultant set of fuzzy rules allows an insight into the linguistic interpretation on the relationship between associated characteristics and the respective song flight duration.
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    Categorial expert systems (2004)
    Oxford, UK and Boston, USA : Blackwell Publishing Ltd
    Expert systems 21 (2004), S. 0 
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    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: Expert systems can be used to determine some objects or consequences from uncertain knowledge by hierarchical categorization. Categorical representation is psychologically motivated and also offers an explanation of how to deal with uncertain knowledge based on counting during approximate reasoning. It is an alternative to other well-known uncertainty calculi. A knowledge base which is used during approximate reasoning is represented by a taxonomical arrangement of verbal categories. Priming eases the formation of the final hypothesis, as more exact possible hypotheses are formed. The approximate reasoning is demonstrated on an expert system ‘Jurassic’ from the field of paleontology for the determination of a dinosaur species. It helps the paleontologist to determine creatures from uncertain knowledge. The system is composed of 423 rules arranged in a directed acyclic graph with a depth of 5. This knowledge is represented by a taxonomical arrangement of verbal categories represented by associative memories.
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    CRAS-CBR: Internal control risk assessment system using case-based reasoning (2004)
    Oxford, UK and Boston, USA : Blackwell Publishing Ltd
    Expert systems 21 (2004), S. 0 
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    ISSN: 1468-0394
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: Information technology and the Internet have been major drivers for changes in all aspects of business processes and activities. They have brought major changes to the financial statements audit environment as well, which in turn has required modifications in audit procedures. There exist certain difficulties, however, with current audit procedures especially for the assessment of the level of control risk. This assessment is primarily based on the auditors' professional judgment and experiences, not on objective rules or criteria.To overcome these difficulties, we propose a prototype decision support model named CRAS-CBR using case-based reasoning to support auditors in making their professional judgment on the assessment of the level of control risk of the general accounting system in the manufacturing industry.To validate the performance, we compare our proposed model with benchmark performances in terms of classification accuracy for the level of control risk. Our experimental results show that CRAS-CBR outperforms a statistical model and staff auditor performance in average hit ratio.
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    Knowledge acquisition in incomplete fuzzy information systems via the rough set approach (2003)
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    Topics: Computer Science
    Notes: Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.
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    Probabilistic approaches to rough sets (2003)
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    Notes: Abstract: Probabilistic approaches to rough sets in granulation, approximation and rule induction are reviewed. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic approximations are defined in a decision-theoretic framework. The problem of rule induction, a major application of rough set theory, is studied in probabilistic and information-theoretic terms. Two types of rules are analyzed: the local, low order rules, and the global, high order rules.
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    A rough set approach to knowledge reduction based on inclusion degree and evidence reasoning theory (2003)
    Oxford, UK and Boston, USA : Blackwell Publishing Ltd
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    Topics: Computer Science
    Notes: Abstract: The theory of rough sets is an extension of set theory for studying intelligent systems characterized by insufficient and incomplete information. We discuss the basic concept and properties of knowledge reduction based on inclusion degree and evidence reasoning theory, and propose a knowledge discovery approach based on inclusion degree and evidence reasoning theory.
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    Acquisition of hierarchy-structured probabilistic decision tables and rules from data (2003)
    Oxford, UK and Boston, USA : Blackwell Publishing Ltd
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    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: The paper is concerned with the creation of predictive models from data within the framework of the variable precision rough set model. It is focused on two aspects of the model derivation: computation of uncertain, in general, rules from information contained in probabilistic decision tables and forming hierarchies of decision tables with the objective of reduction or elimination of decision boundaries in the resulting classifiers. A new technique of creation of a linearly structured hierarchy of decision tables is introduced and compared to tree-structured hierarchy. It is argued that the linearly structured hierarchy has significant advantages over tree-structured hierarchy.
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    A variational approach to intensity approximation for remote sensing images using dynamic neural networks (2003)
    Oxford, UK : Blackwell Publishing
    Expert systems 20 (2003), S. 0 
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    Topics: Computer Science
    Notes: Abstract: In remote sensing image processing, image approximation, or obtaining a high-resolution image from a corresponding low-resolution image, is an ill-posed inverse problem. In this paper, the regularization method is used to convert the image approximation problem into a solvable variational problem. In regularization, the constraints on smoothness and discontinuity are considered, and the original ill-posed problem is thereby converted to a well-posed optimization problem. In order to solve the variational problem, a Hopfield-type dynamic neural network is developed. This neural network possesses two states that describe the discrepancy between a pixel and adjacent pixels, the intensity evolution of a pixel and two kinds of corresponding weights. Based on the experiment in this study with a Landsat TM image free of added noise and a noisy image, the proposed approach provides better results than other methods. The comparison shows the feasibility of the proposed approach.
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    A knowledge processing method for intelligent systems based on inclusion degree (2003)
    Oxford, UK : Blackwell Publishing
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    Topics: Computer Science
    Notes: The probability reasoning method, fuzzy reasoning method, evidential reasoning method and other reasoning methods are main techniques employed in intelligent systems for processing uncertain and vague information. The concept of inclusion degree was proposed earlier and it has been proved that the methods mentioned above are examples of inclusion degrees. In this paper, we introduce type S1 and type S2 inclusion degrees, discuss the relationship between them, and further propose inclusion degrees on interval numbers, divisions, vectors and set vectors. This paper addresses an uncertainty analysis method with different inclusion degrees for intelligent systems and other systems such as fuzzy relational databases.
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    An intelligent agent with layered architecture for operating systems resource management (2003)
    Oxford, UK : Blackwell Publishing
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    Topics: Computer Science
    Notes: Abstract: A software agent is defined as an autonomous software entity that is able to interact with its environment. Such an agent is able to respond to other agents and/or its environment to some degree, and has some sort of control over its internal state and actions. In belief–desire–intention (BDI) theory, an agent's behavior is described in terms of a processing cycle. In this paper, based on BDI theory, the processing cycle is studied with a software feedback mechanism. A software feedback or loop-back control mechanism can perform functions without direct external intervention. A feedback mechanism can continuously monitor the output of the system under control (the target system), compare the result against preset values (goals of the feedback control) and feed the difference back to adjust the behavior of the target system in a processing cycle. We discuss the modeling and design aspects of an autonomous, adaptive monitoring agent with layered control architecture. The architecture consists of three layers: a scheduling layer, an optimizing layer and a regulating layer. Experimental results show that the monitoring agent developed for an e-mail server is effective.
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    A new and efficient ant-based heuristic method for solving the traveling salesman problem (2003)
    Oxford, UK : Blackwell Publishing
    Expert systems 20 (2003), S. 0 
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    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems.
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    A model of a decision support system based on case-based reasoning for third-party logistics evaluation (2003)
    Oxford, UK : Blackwell Publishing
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    Notes: The social demands for third-party logistics (3PL) are further developing the model of supply chain management. The analysis of an effective approach for 3PL service supplier evaluation is given in terms of its direct relation to the operational efficiency and benefit of the service-demanding enterprise as well as its supply chain management. An analysis of the traditional academic theoretical results and practical methods for 3PL supplier selection indicates the deficiencies of this approach. In this paper, a case-based reasoning model framework is postulated for a 3PL evaluation and selection system. This work further expands upon the theoretical basis of this system and its reasoning process by discussing the advantages and practical value of this framework.
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    On a responsive replenishment system: a fuzzy logic approach (2003)
    Oxford, UK and Boston, USA : Blackwell Publishing Ltd
    Expert systems 20 (2003), S. 0 
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    Notes: In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.
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    Providing Websites with capabilities of one–to–one marketing (2003)
    Oxford, UK and Boston, USA : Blackwell Publishing Ltd
    Expert systems 20 (2003), S. 0 
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    Source: Blackwell Publishing Journal Backfiles 1879-2005
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    Notes: A formidable synergy can be obtained by putting expert system technology into the Internet. The modern trend of embedding expert systems into Websites turns out to be very promising, in particular for the field of marketing via the Web. The last two years have seen a growing interest in providing Websites with suitable embedded expert systems for one–to–one marketing. One–to– one marketing means marketing in a personalized way, i.e. marketing in a way that is adaptive to the personal needs of the user. A basic feature of this marketing framework consists in personalized prioritizing of news, i.e. presenting information in an order that is relevant to the specific needs of the current user. If the personalized prioritizing of news is a very useful feature in wired Web, it becomes essential in wireless Web, the promising next generation of the Web.The paper presents a general methodology for personalized prioritizing of news. The methodology integrates decision theory with a deep–knowledge–based user model (i.e. causal knowledge linking user preferences to user goals). The deep–knowledge model of the user is a source of power of the methodology because it allows the system to know (and possibly explain) why the user acts the way he/she acts. Another relevant aspect of the methodology is that the burden of personalization is not placed on the user, and in fact the user does not have to declare his/her needs or interests or goals: they are automatically inferred from his/her profile data. In order to investigate the ideas underlying the proposal, a methodology example has been implemented in a prototype and then tested on real cases in the context of a supercomputing portal.
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    The perverse horrors of user–centric design (2002)
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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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    Chaotic analysis of predictability versus knowledge discovery techniques: case study of the Polish stock market (2002)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 19 (2002), S. 0 
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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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    A prototype store choice and location modelling system using Dempster–Shafer theory (2002)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 19 (2002), S. 0 
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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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    Monitoring the supply of products in a supply chain environment: a fuzzy neural approach (2002)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
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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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    Development of an Intelligent Data-Mining System for a Dispersed Manufacturing Network (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
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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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    An Efficient Expert System for Air Compressor Troubleshooting (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 18 (2001), S. 0 
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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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    Editorial (2001)
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    News (2001)
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    Development of an Expert System Shell Based on Genetic Algorithms for the Selection of the Energy Best Available Technologies and their Optimal Operating Conditions for the Process Industry (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
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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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    A Model-Based Method for an Online Diagnostic Knowledge-Based System (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
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    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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    The Dialoguer: An Interactive Bilingual Interface to a Network Operating System (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
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    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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    Diary (2001)
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    Call for Papers (2001)
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    News (2001)
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    A metagenetic algorithm for information filtering and collection from the World Wide Web (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 18 (2001), S. 0 
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    Source: Blackwell Publishing Journal Backfiles 1879-2005
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    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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    Modeling expert problem solving in a game of chance: a Yahtzee© case study (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 18 (2001), S. 0 
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    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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    Diary (2001)
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    Editorial: Another millennium, another editorial... (2001)
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    Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
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    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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    Intelligent interaction design: the role of human-computer interaction research in the design of intelligent systems (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
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    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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    The Kohonen self-organizing map: an application to the study of strategic groups in the UK hotel industry (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    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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    A fuzzy analytic hierarchy process approach in modular product design (2001)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 18 (2001), S. 0 
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    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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    Book Review (2001)
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    Notes: Book reviewed: The Essence of Expert Systems, Susan J. Chinn
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    News (2001)
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    Diary (2001)
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    Knowledge representation in an activated sludge plant diagnosis system (2000)
    Oxford, UK and Boston, USA : Blackwell Publishers Ltd
    Expert systems 17 (2000), S. 0 
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    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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    COACH: A tutor based on active schemas (1986)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 2 (1986), S. 0 
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    Topics: Computer Science
    Notes: The Coach system, a computer simulation of a human tutor, was constructed with the goal of obtaining a better understanding of how a tutor interprets the student's behavior, diagnoses difficulties, and gives advice. Coach gives advice to a student who is learning a simple computer programming language. Its intelligence is based on a hierarchy of active schemas that represent the tutor's general concepts and on more specific information represented in a semantic network. The coordination of conceptually guided and data-driven processing enables the Coach system to interpret student behavior, recognize errors, and give advice to the student.
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    GENERATING TEXT WITHOUT TREES (1992)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 8 (1992), S. 0 
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    Notes: In this paper, I present an architecture for generating extended text. This architecture is implemented in a system, Salix, which incrementally generates natural language texts whose structure is derived from the domain structure of the subject matter. The architecture is composed of data-driven, domain-independent strategies for producing increments of text. The strategies include metastrategies that combine or choose among all strategies that are applicable at each increment or decide what to do if no strategy applies. Salix's capabilities are demonstrated in generating texts, in the domains of houses and families, that are comparable to descriptions elicited from human speakers. Salix has also been utilized to generate texts about text style (Germain 1991). The approach to text generation presented here is compared to others in the literature along the dimensions of local organization, coherence, focusing, and domain independence. An argument is made for the approach presented here that locally organizes and incrementally generates coherent text.
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    STORY GENERATION BASED ON DYNAMICS OF THE MIND (1992)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 8 (1992), S. 0 
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    Notes: A story generation system based on dynamics of the mind is presented. Semantic theories until now avoided discussing a linkage of language with encyclopedic human knowledge. This paper attacks the problem of how to make clear the complicated, wide-ranging dynamics of the mind and how to connect it to language.First, the background of this study is shown, in which our model of the mind, with both five levels along the concept formation process and eight domains according to the contents of mental activities, is surveyed.Based on this model, then, the dynamics of mind are discussed, along an Aesop fable. A unit of data processing in the mind is called a “module,” and the mental dynamics are considered as a chain activation of those modules, represented by a dynamic network. Next, a method for story generation is discussed. Propositional information of a sentence is embedded in a slot of activated, frame-structured modules, and the discourse structure of a generated story basically depends on the dynamic network. Both the chain activation and the generation processes are verified by experiments.Lastly, residual problems of our research, a comparison with related research, and its applications are discussed.This case study would be expected to give a basis to integrated systems for problem solving, natural language and image understandings, their generations, and intelligent robots.
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    Introduction to the special issue on natural language generation (1991)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 7 (1991), S. 0 
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    SOME NEGLECTED ASPECTS OF THE GENERATION TASK (1992)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 8 (1992), S. 0 
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    Notes: Looking to the future, generators will have more knowledge of language and will have to deal with inputs that are very rich in information. As a result, several problems will become more acute, including selecting what to say at the subproposition level and dealing with interaction among goals and dependencies among choices. This paper explains how these problems arise and why they are hard to handle within traditional architectures for generation. It also discusses why these issues have not been well addressed, including the current lack of demanding applications, excessive emphasis on linguistic traditions, the use of reverse engineering to determine generator inputs, and the tendency to research only one issue at a time.
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    Learning rules for graph transformations by induction from examples (1986)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 2 (1986), S. 0 
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    Notes: The input to the described program, in learning mode, consists of examples of starting graph and result graph pairs. The starting graph is transformable into the result graph by adding or deleting certain edges and vertices. The essential common features of the starting graphs are stored together with specifications of the edges and vertices to be deleted or added. This latter information is obtained by mapping each starting graph onto the corresponding result graph. On subsequent input of similar starting graphs without a result graph, the program, in performance mode, recognizes the characterizing set of features in the starting graph and can perform the proper transformation on the starting graph to obtain the corresponding result graph. The program also adds the production to its source code so that after recompilation it is permanently endowed with the new production. If any feature which lacks the property “ordinary” is discovered in the starting graph and only one example has been given, then there is feedback to the user including a request for more examples to ascertain whether the extraordinary property is a necessary part of the situation.
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    NOTE OF APPRECIATION FROM THE EDITORS (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
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    IT'S ABOUT TIME: AN INTRODUCTION TO THE SPECIAL ISSUE ON TEMPORAL REPRESENTATION AND REASONING (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
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  • 185
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    EFFICIENT TEMPORAL REASONING IN THE CACHED EVENT CALCULUS (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This article deals with the problem of providing Kowalski and Sergot's event calculus, extended with context dependency, with an efficient implementation in a logic programming framework. Despite a widespread recognition that a positive solution to efficiency issues is necessary to guarantee the computational feasibility of existing approaches to temporal reasoning, the problem of analyzing the complexity of temporal reasoning programs has been largely overlooked. This article provides a mathematical analysis of the efficiency of query and update processing in the event calculus and defines a cached version of the calculus that (i) moves computational complexity from query to update processing and (ii) features an absolute improvement of performance, because query processing in the event calculus costs much more than update processing in the proposed cached version.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00267.x
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  • 186
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    TEMPORAL REASONING WITH THE MODAL LOGIC Z (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This article presents a formal theory of concurrent actions that handles the qualification, ramification, and frame problems. The theory is capable of temporal explanation, i.e., reasoning forward and backward. The approach uses the modal logic Z to extend the work of Lifschitz and Rabinov on miracle-based temporal reasoning. The advantages of miracles for describing unknown actions are augmented with the ability to handle concurrent actions that can provide for the most economical explanation of state changes. For temporal explanation problems restricted to finite domains, it has a worst-case exponential decision procedure. The theory is as general as first-order logic in what it can express as preconditions and consequences of actions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00269.x
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  • 187
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    SOLVING NINE MEN'S MORRIS (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: The game of Nine Men's Morris is a draw. We obtained this result using a combination of endgame databases (1010 states) and search. Our improved algorithm for computing endgame databases allowed the game to be solved on a personal computer. Other games have been solved using knowledge-based methods to dramatically prune the search tree. Nine Men's Morris does not seem to profit from such methods, making it the first nontrivial game solved in which almost the entire state space has to be considered.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00251.x
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  • 188
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    LEARNING PLAYING STRATEGIES IN CHESS (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: It is believed that chess masters use pattern-based knowledge to analyze a position, followed by a pattern-based controlled search to verify or correct the analysis. This paper describes a first-order system called PAL that can learn patterns in the form of Horn clauses from simple example descriptions and general purpose knowledge. It is shown how PAL can leam chess patterns that are beyond the learning capabilities of current inductive systems. The patterns learned by PAL can be used for analysis of positions and for the construction of playing strategies. By taking the learned patterns as attributes for describing examples, a set of rules which decide whether a Pawn can safely be promoted without moving the King in a King and Pawn vs King endgame, is automatically constructed with a similarity-based learning algorithm. Similarly, a playing strategy for the King and Rook vs King endgame is automatically constructed with a simple learning algorithm by following traces of games and using the patterns learned by PAL. Limitations of first-order systems, PAL imparticularly, are exposed in domains where a large number of background definitions may be required for induction. Conclusions and future research directions are given.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00253.x
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  • 189
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    PERFECT RECALL AND PRUNING IN GAMES WITH IMPERFECT INFORMATION (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Games with imperfect information are an interesting and important class of games. They include most card games (e.g., bridge and poker) as well as many economic and political models. Here we investigate algorithms for findi ng the simplest form of a solution (a pure-strategy equilibrium point) to imperfect information games expressed in their extensive (game tree) form. We introduce to the artificial intelligence community a classic algorithm, due to Wilson, that solves one-player games with perfect recall. Wilson's algorithm, which we call iMP-minimax, runs in time linear in the size of the game-tree searched. In contrast to Wilson's result, Koller and Meggido have shown that finding a pure-strategy equilibrium point in one-player games without perfect recall is NP-hard. Here, we provide another contrast to Wilson's result–we show that in games with perfect recall but more than one player, finding a pure-strategy equilibrium point, given that such an equilibrium point exists, is NP-hard.Our second contribution is to present a pruning technique for Wilson's IMP-minimax algorithm to make the latter more tractable. We call this new algorithm IMP-alpha-beta. We provide a theoretical framework (model) and analyze IMP-alpha-beta in that model. IMP-alpha-beta is of direct value for one-player, perfect-recall games. It also has strong potential for other imperfect information games, as it is a natural (but as yet untested) heuristic in those cases.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00256.x
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  • 190
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    A STRENGTHENED ALGORITHM FOR TEMPORAL REASONING ABOUT PLANS (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Allen's interval algebra has been shown to be useful for representing plans. We present a strengthened algorithm for temporal reasoning about plans, which improves on straightforward applications of the existing reasoning algorithms for the algebra. This is made possible by viewing plans as both temporal networks and hierarchical structures. The temporal network view allows us to check for inconsistencies as well as propagate the effects of new temporal constraints, whereas the hierarchical view helps us to get the strengthened results by taking into account the dependency relationships between actions.We further apply our algorithm to the process of plan recognition through the analysis of natural language input. We show that such an application has two useful effects: the temporal relations derived from the natural language input can be used as constraints to reduce the number of candidate plans, and the derived constraints can be made more specific by combining them with the prestored constraints in the plans being recognized.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00265.x
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  • 191
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    KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00252.x
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  • 192
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    A PLANNING APPROACH TO DECLARER PLAY IN CONTRACT BRIDGE (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Although game-tree search works well in perfect-information games, it is less suitable for imperfect-information games such as contract bridge. The lack of knowledge about the opponents’ possible moves gives the game tree a very large branching factor, making it impossible to search a significant portion of this tree in a reasonable amount of time.This paper describes our approach for overcoming this problem. We represent information about bridge in a task network extended to represent multi-agency and uncertainty. Our game-playing procedure uses this task network to generate game trees in which the set of alternative choices is determined not by the set of possible actions, but by the set of available tactical and strategic schemes.We have tested this approach on declarer play in the game of bridge, in an implementation called Tignum 2. On 5000 randomly generated notrump deals, Tignum 2 beat the strongest commercially available program by 1394 to 1302, with 2304 ties. These results are statistically significant at the α= 0.05 level. Tignum 2 searched an average of only 8745.6 moves per deal in an average time of only 27.5 seconds per deal on a Sun SPARCstation 10. Further enhancements to Tignum 2 are currently underway.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00255.x
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  • 193
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    NOTE OF APPRECIATION FROM THE EDITORS (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 11 (1995), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1995.tb00048.x
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  • 194
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    A STRATEGIC METAGAME PLAYER FOR GENERAL CHESS-LIKE GAMES (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 12 (1996), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper introduces METAGAMER, the first program designed within the paradigm of Metagame-playing (Metagame). This program plays games in the class of symmetric chess-like games, which includes chess, Chinese chess, checkers, draughts, and Shogi. METAGAMER takes as input the rules of a specific game and analyzes those rules to construct an efficient representation and an evaluation function for that game; they are used by a generic search engine. The strategic analysis performed by METAGAMER relates a set of general knowledge sources to the details of the particular game. Among other properties, this analysis determines the relative value of the different pieces in a given game. Although METAGAMER does not learn from experience, the values resulting from its analysis are qualitatively similar to values used by experts on known games and are sufficient to produce competitive performance the first time METAGAMER plays a new game. Besides being the first Metagame-playing program, this is the first program to have derived useful piece values directly from analysis of the rules of different games. This paper describes the knowledge implemented in METAGAMER, illustrates the piece values METAGAMER derives for chess and checkers, and discusses experiments with METAGAMER on both existing and newly generated games.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1996.tb00258.x
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  • 195
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    INTRACTABILITY IN THE ALLEN AND KOOMEN PLANNER (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 11 (1995), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: The Allen and Koomen planner is intractable in two ways: the Allen interval algebra is an intractable temporal reasoner, and the collapsing problem introduces a large branching factor in the search space for a solution plan. We define independence and dependence for networks to address both problems. Independence is used to find a decomposition of an interval network, and dependence is used to focus search when faced with the collapsing problem.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1995.tb00049.x
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    ON COMPUTING THE MINIMAL LABELS IN TIME POINT ALGEBRA NETWORKS (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 11 (1995), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: We analyze the problem of computing the minimal labels for a network of temporal relations in point algebra. Van Beek proposes an algorithm for accomplishing this task, which takes O(max(n3, n2 m)) time (for n points and m ≠-relations). We show that the proof of the correctness of this algorithm given by van Beek and Cohen is faulty, and we provide a new proof showing that the algorithm is indeed correct.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1995.tb00043.x
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    A NEW METHOD FOR INFLUENCE DIAGRAM EVALUATION (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 11 (1995), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: As influence diagrams become a popular representational tool for decision analysis, influence diagram evaluation attracts more and more research interests. In this article, we present a new, two-phase method for influence diagram evaluation. In our method, an influence diagram is first mapped onto a decision graph and then the analysis is carried out by evaluating the decision graph. Our method is more efficient than Howard and Matheson's because, among other reasons, our method generates a much smaller decision graph for the same influence diagram. Like those most recent algorithms reported in the literature, our method also provides a clean interface between influence diagram evaluation and Bayesian net evaluation. Consequently, various well-established algorithms for Bayesian net evaluation can be used in influence diagram evaluation. Furthermore, our method has a few unique merits. First, it takes advantage of asymmetry in influence diagrams to avoid unnecessary computation. Second, by using heuristic search techniques, it provides an explicit mechanism for using heuristic information that may be available in a domain-specific form. These additional merits make our method more efficient than the current algorithms in general. Finally, by using decision graphs as an intermediate representation, the value of perfect information can be computed in a more efficient way.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1995.tb00046.x
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    VAGUENESS AND UNCERTAINTY: A ROUGH SET PERSPECTIVE (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 11 (1995), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Vagueness and uncertainty have attracted the attention of philosophers and logicians for many years. Recently, AI researchers contributed essentially to this area of research. Fuzzy set theory and the theory of evidence are seemingly the most appealing topics. On this note we present a new approach, based on the rough set theory, for looking to these problems. The theory of rough sets seems a suitable mathematical tool for dealing with problems of vagueness and uncertainty. This paper is a modified version of the author's lecture titled “An inquiry into vagueness and uncertainty,” which was delivered at the AI Conference in Wigry (Poland), 1994.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1995.tb00029.x
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    THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 11 (1995), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This paper presents results of experiments showing how machine learning methods arc useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to lest how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1995.tb00032.x
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    LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 11 (1995), S. 0 
    Details
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Knowledge discovery in databases, or dala mining, is an important direction in the development of data and knowledge-based systems. Because of the huge amount of data stored in large numbers of existing databases, and because the amount of data generated in electronic forms is growing rapidly, it is necessary to develop efficient methods to extract knowledge from databases. An attribute-oriented rough set approach has been developed for knowledge discovery in databases. The method integrates machine-learning paradigm, especially learning-from-examples techniques, with rough set techniques. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause-effect relationship among the attributes in the database is analyzed using rough set techniques, and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute-oriented induction combined with rough set theory provide an efficient and effective mechanism for knowledge discovery in database systems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-8640.1995.tb00035.x
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