Generalizing inference rules in a coherence-based probabilistic default reasoning

详细信息	查看全文 | 推荐本文 |

• 作者：Angelo Gilio ; ^{angelo.gilio@sbai.uniroma1.it} ; ^{gilioa@tiscali.it}
• 关键词：Coherence principle ; Probabilistic default reasoning ; Generalization of inference rules ; Lower and upper bounds ; t-Norms ; t-Conorms
• 刊名：International Journal of Approximate Reasoning
• 出版年：2012
• 期刊代码：76_0888613x
• 类别：et
• 出版时间：April, 2012
• 卷：53
• 期：3
• 页码：413-434
• 文件大小：421 K

摘要

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.