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Generalizing inference rules in a coherence-based probabilistic default reasoning. (English) Zbl 1242.68330
Summary: In this paper we first recall some notions and results on the coherence-based probabilistic treatment of uncertainty. Then, we deepen some probabilistic aspects in nonmonotonic reasoning, by generalizing OR, CM, and cut rules. We also illustrate the degradation of these inference rules when the number of premises increases. Finally, we show that the lower bounds obtained when applying OR and quasi-conjunction inference rules coincide, respectively, with Hamacher and Lukasiewicz t-norms; the upper bounds in both rules coincide with Hamacher t-conorm.

MSC:
68T37 Reasoning under uncertainty in the context of artificial intelligence
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