R.R. Leitch

Heriot-Watt University, Edinburgh, SCT, United Kingdom

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Publications (43)12.31 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: The authors consider models to be executable descriptions of the real world, that is a model can be used to predict or analyse properties of the system. Simulation and reasoning systems, which may be derived from traditional or AI approaches, are used to execute these models. Given the plethora of modelling techniques available which cope well with certain, but not other, contexts, it is evident that there is no `best model' covering all situations: a model is correct if it satisfies its purpose no less and no more. The desires of the user of a modelling system are always moderated by the availability of techniques permitting these desires to be met. To alleviate the difficulties associated with this requires a methodology to guide the user to the best model and simulation technique to meet his needs. A primary requirement in the construction of such a methodology is a comprehensive and understandable classification of the choices inherent in the construction of a model
    IEE Proceedings - Control Theory and Applications 10/1999; · 1.05 Impact Factor
  • M. Ravindranathan, R. Leitch
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    ABSTRACT: Intelligent control of complex industrial processes requires that knowledge from a variety of sources be used to maintain control over an extended set of operating conditions. It is asserted that to maintain the integrity of such heterogeneous knowledge, it should be encoded in distinct models. Control then consists of selecting and executing the most appropriate model for a given situation. Adaptive intelligent control can then be implemented by developing switching strategies that allow a trade-off between the various model properties. A prototype system (MuRaLi) is presented that has multiple models based on three primitive dimensions: precision, scope and generality. Generality is realised through three different knowledge representation mechanisms: procedures, rules and equations. Homogeneous control consists of constant generality and variable precision and scope to generate the most appropriate control action. Heterogeneous control consists of potential variations in all three dimensions. Simple switching strategies are investigated for both forms of control. The system has been applied to the control of a simulated 800 MW thermal power plant. Examples of homogeneous and heterogeneous control are given, with experimental results of adaptation based on the proposed switching strategies
    IEE Proceedings - Control Theory and Applications 12/1998; · 1.05 Impact Factor
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    ABSTRACT: The desires of the user of a modelling system are always moderated by the availability of techniques permitting these desires to be met. To alleviate the difficulties associated with this requires a methodology to guide the user to the best model and simulation technique to meet his needs. A primary requirement in the construction of such a methodology is a comprehensive and understandable classification of the choices inherent in the construction of a model and a categorisation of existing techniques in the light of this classification. This paper has presented such a classification. The modelling process was presented as consisting of three modelling choices involving the definition or adjustment of a number of model properties
    Physical Modelling as a Basis for Control (Digest No: 1996/042), IEE Colloquium on; 03/1996
  • Integrated Computer Aided Engineering 09/1995; 2(3):203-217. · 3.37 Impact Factor
  • A. Steele, R. Leitch
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    ABSTRACT: This paper presents details of an architecture for parameter identification, based on qualitative modelling and reasoning techniques, which is used as the basis for a time-constrained model-based diagnosis system. Section 2 presents the motivations for this work and outlines the QPID (Qualitative Parameter Identification for Diagnosis) architecture. Section 3 gives details of the Strategist module of QPID, and how this meta-level module exercises control over the reasoning at the object-level to attempt to meet time-constraints. Finally, section 4 presents the evaluation and conclusions of this interim work into the use of qualitative techniques in parameter identification for diagnosis
    Qualitative and Quantitative Modelling Methods for Fault Diagnosis, IEE Colloquium on; 05/1995
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    ABSTRACT: The development of application systems for fault diagnosis has attracted worldwide interest in many different research areas. In particular, advanced techniques for finding faults through the use of explicit structural and/or behavioural models of the physical system to be diagnosed have been developed both in the area of control engineering and in the field of artificial intelligence. Although many approaches to creating model-based diagnostic systems (MBDS) exist, as yet, no clear methodology is available for the selection of an appropriate approach to solve individual given diagnostic problems. We have therefore been developing a specification methodology that essentially comprises a taxonomy of diagnostic tasks, a taxonomy of model-based systems, and a set of guidelines that provides a mapping from the former to the latter, Our aim is to provide a method by which existing MBDS tools and techniques may be combined within a generic architecture in a principled manner to produce effective diagnostic systems for given applications. The structure of the methodology has been derived in part from the top level architectural design of ARTIST, a generic model-based diagnostic toolkit that combines a wide variety of model based diagnostic (MBD) tools. This architecture is based upon the three main types of knowledge that are necessary for the construction of model based diagnostic systems
    Real-Time Knowledge Based Systems, IEE Colloquium on; 03/1995
  • G.J Wyatt, R.R Leitch, A.D Steele
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    ABSTRACT: Traditional quantitative methods of analysis and simulation are compared with recently developed techniques in qualitative simulation by using as a case-study a simple dynamic model of the interacting markets for housing and mortgages. Analysis by the different techniques shows that while the qualitative simulation requires less detailed models, of the precision normally available in practice, it results in ambiguous descriptions of behaviour that for certain initial conditions can obscure the true behaviour. By contrast, quantitative simulation produces a unique precise behaviour, but in requiring excessively specific information of the modeller it may produce an inaccurate if precise outcome.
    Decision Support Systems 01/1995; 15(2):105-113. · 2.20 Impact Factor
  • Q. Shen, B. Mulvihill, R. Leitch
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    ABSTRACT: This paper presents a preliminary approach to providing an explanation facility for model-based diagnostic systems by the use of causal ordering. An explanation algorithm is described that allows explanations to be automatically generated for diagnostic findings by searching through the causal graph derived from the structural and behavioural model of the physical system under diagnosis. Typical experimental results are given, demonstrating the successful use of the explanation algorithm
    Intelligent Systems Engineering, 1994., Second International Conference on; 10/1994
  • Q. Shen, R.R. Leitch, A.D. Steele
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    ABSTRACT: Utilising multiple-model descriptions requires that the relationships between the various models be well-defined and can be generated systematically from a reference model. We present a generic model harness, for component-based models, that is based on a set of fundamental representational primitives that are directly related to a classification of basic model properties. This supports the customisation of the harness for a particular model and also the systematic generation of multiple models. Examples of the resulting models and their corresponding behaviours are presented for a laboratory-scale system rig
    Intelligent Systems Engineering, 1994., Second International Conference on; 10/1994
  • R.R. Leitch, Q. Shen
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    ABSTRACT: The motivations for qualitative modelling and fuzzy modelling are almost identical: to cope with the complexities in the modelling of real systems. However, their developments have been independent, distinct and complementary. The synthesis of these techniques has required a re-appraisal of exactly what model properties are used in these techniques. We argue that precision and uncertainty are distinct concepts. Qualitative modelling deals with abstract imprecise models whilst fuzzy modelling copes with uncertain imprecise or precise models, With this clarification we have developed a fuzzy qualitative simulation system, an outline of which is given in this paper. We believe that such combinations of fuzzy and qualitative methods are the natural development of Zadeh's original proposal for intelligent reasoning about complex systems
    Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on; 07/1994
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    ABSTRACT: Model-based diagnosis is regarded by many as a way to overcome the limitations of first-generation knowledge-based systems which perform fault classification by means of empirical symptom-failure associations. Many different approaches to model-based diagnosis exist. The ESPRIT ARTIST project, focusing on the development of model-based techniques for diagnosis of industrial systems, has tried to integrate these approaches within a common generic architecture so that a given application system could be generated by composing a particular diagnostic system from a set of predeveloped and hence reusable modules. There is an overview of the main results of the ARTIST project, its generic architecture and the three diagnostic systems developed and tested within the project. An industrial application to management of electrical transmission networks is presented
    Intelligent Systems Engineering 02/1994;
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    Q. Shen, R. Leitch
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    ABSTRACT: An approach is described that utilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, providing three significant advantages over existing techniques. Firstly, it allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The adoption of fuzzy sets also allows common-sense knowledge to be represented in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and reasoned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both strength and sign information can be represented by fuzzy relations holding against two or multivariables. Thirdly, the quantity space allows ordering information on rates of change to be used to compute temporal durations of the state and the possible transitions. Thus, an ordering of the evolution of the states and the associated temporal durations are obtained. This knowledge is used to develop an effective temporal filter that significantly reduces the number of spurious behaviors
    IEEE Transactions on Systems Man and Cybernetics 08/1993;
  • Julie-Ann Sime, R.R. Leitch
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    ABSTRACT: This paper describes the basis of a specification methodology for building Intelligent Training Systems in Industrial Environments (ESPRIT project 2615, ITSIE). The specification methodology determines the mapping of specific training requirements onto tools and techniques for implementation based upon an analysis of the training requirements and of the nature of the domain knowledge.A task analysis is carried out on the training requirements to determine a number of specific training objectives. These objectives are described in terms of the required level of behaviour and knowledge necessary to achieve the objective. This description leads to the determination of a primitive mode of instruction, which promotes: rote, inductive or deductive learning. This decomposition is used to identify relevant tools and techniques for domain knowledge representations and for didactics and diagnosis within the tutor of the training system.
    Computers & Education. 02/1993; 20(1):73–80.
  • R R Leitch, Yiu Kwong Wong, G J Wyatt
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    ABSTRACT: For many tasks, it may be more appropriate to reason with qualitative models which are an inexact representation of the real world than to use the more powerful classical tools of differential or difference equations. Qualitative methods allow us to build models which do not necessarily incorporate assumptions, such a linearity or constancy over time. Even with such imprecise knowledge, there is enough information in a qualitative description to support qualitative simulation, which predicts the possible qualitative behaviours of an economic system. Copyright 1993 by Taylor and Francis Group
    Economic Systems Research 02/1993; 5(4):377-93. · 2.43 Impact Factor
  • Q. Shen, R.R. Leitch, G.M. Coghill
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    ABSTRACT: The theory of fuzzy sets and the development of qualitative modelling have had similar motivations: coping with complexity in reasoning about the behaviour of physical systems. The paper presents a synthesis of these techniques, providing a fuzzy qualitative modelling method for performing qualitative simulation that offers significant advantages over existing qualitative simulation methods. The resulting simulation algorithm is termed FuSim hereafter. This development makes a significant contribution towards the full-scale industrial applications of qualitative modelling. The paper shows a typical example of utilising fuzzy qualitative models in fault diagnosis of continuous dynamic systems, based on an iterative search technique
    Two Decades of Fuzzy Control - Part 2, IEE Colloquium on; 02/1993
  • J.-A. Sime, R. Leitch
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    ABSTRACT: As physical systems becomes larger and more complex, it is more and more difficult to model them, and to reason about their behaviour. Multiple models can be used to reduce the complexity of a model to a manageable size. Each model representing a particular aspect of the system. This is done by only modelling features that are relevant to the current task. The paper provides a coherent foundation for the dimensions along which these models vary, within the context of instruction about a physical system. How these models may enhance instruction is discussed. In particular qualitative modelling through cognitive apprenticeship. The modelling dimensions are illustrated through the modelling of an experimental Process Rig, for the purposes of building an intelligent training system
    Intelligent Systems Engineering, 1992., First International Conference on (Conf. Publ. No. 360); 09/1992
  • R.R. Leitch, P. Ponnapalli, A.F. Slater
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    ABSTRACT: The ESPRIT project 2615 ITSIE, aims to utilise ITS techniques to develop a generic framework for the development of training systems. A major aspect of this project is the use of the qualitative modelling techniques to meet the needs of current training systems. The project has recognised the importance of clearly establishing the types of skills and `expertise' required of the trainee. The authors believe that this expertise exists in various forms and should be supported by appropriate representational tools. A systematic classification of domain knowledge for intelligent training is presented as used within the ITSIE project. Based on earlier studies on the classification of the behaviour of humans in industrial environments, it is proposed that the domain knowledge be classified into two main categories: domain expertise and domain simulation. The proposed classification and tools provide a coherent way of representing domain knowledge in ITS and allow the use of multiple models in a model switching framework for effective training
    Intelligent Systems Engineering, 1992., First International Conference on (Conf. Publ. No. 360); 09/1992
  • Q. Shen, R.R. Leitch
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    ABSTRACT: The paper presents several innovations in the development of techniques for diagnosing continuous dynamic systems based on qualitative models. It argues that, to diagnose such physical systems, a mechanism for modelling and simulating continuous dynamic behaviours, a method for synchronous tracking of the dynamic behaviours, a metric for detecting degradation of the continuous behaviours and for matching predictions with observations must be developed. The paper provides a particular realisation of these techniques, following a proposal for iteratively searching for fault models against possible modelling dimensions. The process of how such a diagnostic system finds faults is demonstrated by a simple example
    Intelligent Systems Engineering, 1992., First International Conference on (Conf. Publ. No. 360); 09/1992
  • R. Leitch
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    ABSTRACT: It is argued that the development of artificial intelligence techniques is bringing about fundamental changes in the way we represent and reason about the physical world. From a control engineering perspective, such methods offer a significant extension of the available method for systems modelling, and hence open up exciting prospects for the diversification of control methods to other application areas, e.g. automated fault diagnosis, simulation and training. However, such diversification brings with it the need to clearly establish the principles, and hence the limitations, behind each technique. Accordingly, the author proposes a classification of system models in terms of their knowledge classes and characteristics, and relates these to existing approaches to the use of AI methods in control. Such a classification is a necessary precursor to developing a methodological approach to identifying the most appropriate technique for a given generic class of applications
    Computing and Control Engineering 08/1992; · 0.16 Impact Factor
  • M.O. Tokhi, R.R. Leitch
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    ABSTRACT: An analysis of the process of field cancellation in a three-dimensional non-dispersive propagation medium is given. This analysis is presented from the perspective of the design of active noise control (ANC) systems for broadband noise emanating from compact sources. The analysis of cancellation is presented in parametric terms, both in time and in frequency, leading to a three-dimensional description of cancellation that is suitable for determining the geometrical arrangement for a given degree of cancellation, and consequently can be used for the design of ANC systems. Such parameters can also be used to determine the performance of the resulting ANC system.
    Journal of Sound and Vibration 06/1992; 155(3):497–514. · 1.61 Impact Factor