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Empirical Modelling and the Foundations of Artificial Intelligence

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Computation for Metaphors, Analogy, and Agents (CMAA 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1562))

Abstract

This paper proposes Empirical Modelling (EM) as a possible foundation for AI research outside the logicist framework. EM offers principles for constructing physical models, typically computer-based, by construing experience in terms of three fundamental concepts: observables, dependency and agency. EM is discussed in the context of critiques of logicism drawn from a variety of sources, with particular reference to the five foundational issues raised by Kirsh in his paper Foundations of AI: the Big Issues (AI, 47:3-30, 1991), William James’s Essays on Radical Empiricism (Bison Books, 1996), and the controversy surrounding formal definitions for primitive concepts such as metaphor and agent that are recognised as fundamental for AI. EM principles are motivated and illustrated with reference to a historic railway accident that occurred at the Clayton Tunnel in 1861.

The principal thesis of the paper is that logicist and non-logicist approaches to AI presume radically difierent ontologies. Specifically, EM points to a fundamental framework for AI in which experimentally guided construction of physical artefacts is the primary mode of knowledge representation. In this context, propositional knowledge is associated with phenomena that are perceived as circumscribed and reliable from an objective ‘third-person’ perspective. The essential need to incorporate subjective ‘first-person’ elements in an account of AI, and the role that commitment plays in attaching an objective meaning to phenomena, are seen to preclude a hybrid approach to AI in the conventional sense.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK

    Meurig Beynon

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  1. Meurig Beynon
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Editors and Affiliations

  1. Faculty of Engineering and Information Sciences, University of Hertfordshire, College Lane, Hatfield Herts, AL10 9AB, UK

    Chrystopher L. Nehaniv

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© 1999 Springer-Verlag Berlin Heidelberg

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Beynon, M. (1999). Empirical Modelling and the Foundations of Artificial Intelligence. In: Nehaniv, C.L. (eds) Computation for Metaphors, Analogy, and Agents. CMAA 1998. Lecture Notes in Computer Science(), vol 1562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48834-0_18

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  • DOI: https://doi.org/10.1007/3-540-48834-0_18

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