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Model Construction for Knowledge-Intensive Engineering Tasks

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Advances of Computational Intelligence in Industrial Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 116))

Summary

The construction of adequate models to solve engineering tasks is a field of paramount interest. The starting point for an engineering task is a single system, S, or a set of systems, S, along with a. shortcoming of information, often formulated as a question:

  • Which component is broken in S? (diagnosis ∼ analysis)

  • How does S react on the input u? (simulation ∼ analysis)

  • Does a system with the desired functionality exist in S? (design ∼ synthesis)

If such an analysis or synthesis question shall be answered automatically, both adequate algorithmic models along with the problem solving expertise of a human problem solver must be operationalized on a computer. Often, the construction of an adequate model turns out to be the key challenge when tackling the engineering task. Model construction – also known as model creation, model formation, model finding, or model building – is an artistic discipline that highly depends on the reasoning job in question.

Model construction can be supported by means of a computer, and in this chapter we present a comprehensive view on model construction, characterize both existing and new paradigms, and give examples for the state of the art of the realization technology. Our contributions are as follows:

  • In Sect. 2 we classify existing model construction approaches with respect to their position in the model hierarchy. Nearly all of the existing methods support a top-down procedure of the human modeler; they can be characterized as being either structure-defining (top), structure-filling (middle), or structure propagating (down).

  • Domain experts and knowledge engineers rarely start from scratch when constructing a new model; instead, they develop an appropriate model by modifying an existing one. Following this observation we analyzed various projects and classified the found model construction principles as model simplification, model compilation, and model reformulation. In Sect. 3 we introduce these principles as horizontal modeling construction and provide a generic characterization of each.

  • Section 4 presents real-world case studies to show horizontal model construction principles at work. The underlying technology includes, among others, hybrid knowledge representations, case-based as well as rule-based reasoning, and machine learning.

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Author information

Authors and Affiliations

  1. Faculty of Media, Media Systems, Bauhaus University Weimar, 99421, Weimar, Germany

    Benno Stein

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  1. Benno Stein
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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Ying Liu

  2. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798

    Aixin Sun  & Ee-Peng Lim  & 

  3. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117576

    Han Tong Loh  & Wen Feng Lu  & 

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Stein, B. (2008). Model Construction for Knowledge-Intensive Engineering Tasks. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_7

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  • DOI: https://doi.org/10.1007/978-3-540-78297-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78296-4

  • Online ISBN: 978-3-540-78297-1

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