Empirical Modelling and the Foundations of Artificial Intelligence

Abstract

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 different 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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“…It follows from this that the representation of such beliefs cannot take the form of a propositional specification, but must involve the direct use of representations, such as ISMs, that are experiential in nature. A fuller discussion and justification for this point of view is to be found in the reappraisal of the foundations of computation and intelligence in [Bey99], and is supported by empirical evidence gained from the practical application of EM by several hundred Computer Science students.…”

“…Adopting the non-logicist perspective of [Bey99], an enormous body of empirical evidence is needed to support beliefs about the objectivity of different agent perceptions, for instance, or to endorse such a sophisticated conception as autopoiesis. Even objects and processes are richly informed by experience: objects through familiarity built up through acquaintance with many occurrences, and processes through repeated sequences of events in which such issues as commonality of perception, extending to an awareness of "location in an abstract process" on the part of some of the participants, play an essential part.…”

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