Intensional FOL for reasoning about probabilities and probabilistic logic programming Online publication date: Tue, 08-May-2018
by Zoran Majkić; Bhanu Prasad
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 11, No. 1, 2018
Abstract: It is important to have a logic, both for computation of probabilities and for reasoning about probabilities, with well-defined syntax and semantics. The current approaches, which are based on Nilsson's probability structures/logics as well as linear inequalities, to reason about probabilities, have some deficiencies. In this research, we have presented a complete revision of those approaches and have shown that the logic for reasoning about probabilities can be naturally embedded into a 2-valued intensional first-order logic (FOL) with intensional abstraction, by avoiding current ad-hoc system composed of two different 2-valued logics: one for the classical propositional logic at a lower-level and a new one, at a higher-level, for probabilistic constraints with probabilistic variables. The theoretical results that are obtained are applied to probabilistic logic programming.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook