Document Type

Contribution to Book

Publication Date

2013

Publication Title

Coherence: Insights from Philosophy, Jurisprudence and Artificial Intelligence

Volume

107

First Page

187

Last Page

201

DOI

10.1007/978-94-007-6110-0_10

Keywords

artificial neural networks, case-based reasoning, coherence

Abstract

A simple recurrent artificial neural network (ANN) is used to classify situations as permissible or impermissible. The trained ANN can be understood as having set up a similarity space of cases at the level of its internal or hidden units. An analysis of the network’s internal representations is undertaken using a new visualization technique for state space approaches to understanding similarity. Insights from the literature on moral philosophy pertaining to contributory standards will be used to interpret the state space set up by the ANN as being structured by implicit reasons. The ANN, on its own, is not capable of explicitly representing or offering reasons to itself or others. That said, the low level similarity space set up by the network could be made available to higher order processes that exploit it for case-based reasoning. It is argued that for normative purposes, similarity could be seen as a contributor to procedural coherence in case-based reasoning and local forms of substantive coherence, but not to global forms of coherence given the computational complexity of managing those more ambitious forms of coherence.

Comments

Case Classification, Similarities, Spaces of Reasons, and Coherences is part of the series Law and Philosophy (vol.107): Coherence: Insights from Philosophy, Jurisprudence and Artificial Intelligence

Published by Springer Netherlands, Copyright Springer Science+Business Media Dordrecht.

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