Abstract
Cognitive processes are often modelled in computational terms. Can this still be done if only minimal assumptions are made about any sort of representation of reality? Is there a purely knowledge-based theory of computation that explains the key phenomena which are deemed to be computational in both living and artificial systems as understood today? We argue that this can be done by means of techniques inspired by the modelling of dynamical systems. In this setting, computations are defined as curves in suitable metaspaces and knowledge is generated by virtue of the operation of the underlying mechanism, whatever it is. Desirable properties such as compositionality will be shown to fit naturally. The framework also enables one to formally characterize the computational behaviour of both knowledge generation and knowledge recognition. The approach may be used in identifying when processes or systems can be viewed as being computational in general. Several further questions pertaining to the philosophy of computing are considered.
Notes
- 1.
The Tarski–Kantorovich fixed point theorem states the following: Let \(\langle X,\le \rangle \) be a cpo and let \(H: X \rightarrow X\) be chain-continuous. If there is an \(x \in X\) such that \(x \le H(x)\), then \(x'=\sup _n H^n(x)\) is a fixpoint and in fact the least fixpoint of H among all y with \(y \ge x\). For a proof see e.g. [22]. Chain-continuity is also known as Scott-continuity.
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The work of the second author was partially supported by ICS AS CR fund RVO 67985807 and the Czech National Foundation Grant No. 15-04960S.
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van Leeuwen, J., Wiedermann, J. (2017). Knowledge, Representation and the Dynamics of Computation. In: Dodig-Crnkovic, G., Giovagnoli, R. (eds) Representation and Reality in Humans, Other Living Organisms and Intelligent Machines. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-43784-2_5
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