Defaults and Infinitesimals: Defeasible Inference by Nonarchimedean Entropy-Maximization

Autor:	Weydert, Emil
Rok vydání:	2013
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	We develop a new semantics for defeasible inference based on extended probability measures allowed to take infinitesimal values, on the interpretation of defaults as generalized conditional probability constraints and on a preferred-model implementation of entropy maximization. Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1302.4988 Zobrazit plný text záznamu View this record from Arxiv