Graduate studies at Western
Journal of Applied Logic 2 (4):469-93 (2004)
|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 relation|
|Keywords||statistical default logic uncertain reasoning knowledge representation non-monotonic reasoning|
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