Elsevier

Journal of Applied Logic

Volume 2, Issue 4, December 2004, Pages 469-493
Journal of Applied Logic

Epistemology and artificial intelligence

https://doi.org/10.1016/j.jal.2004.07.007Get rights and content
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Abstract

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relations.

Keywords

Statistical default logic
Non-monotonic reasoning
Epistemic closure
Logic programming
Uncertainty
Knowledge representation

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