Interval –valued probabilistic logic for logic programs

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Interval-Valued Neural Multi-adjoint Logic Programs

The framework of multi-adjoint logic programming has shown to cover a number of approaches to reason under uncertainty, imprecise data or incomplete information. In previous works, we have presented a neural implementation of its fix-point semantics for a signature in which conjunctors are built as an ordinal sum of a finite family of basic conjunctors (Gödel and Lukasiewicz t-norms). Taking in...

متن کامل

Two-Valued Logic Programs

We define a nonmonotonic formalism that shares some features with three other systems of nonmonotonic reasoning—default logic, logic programming with strong negation, and nonmonotonic causal logic—and study its possibilities as a language for describing actions. 1998 ACM Subject Classification D.1.6 Logic Programming

متن کامل

Temporal Probabilistic Logic Programs

There are many applications where the precise time at which an event will occur (or has occurred) is uncertain. Temporal probabilistic logic programs (TPLPs) allow a programmer to express knowledge about such events. In this paper, we develop a model theory, xpoint theory, and proof theory for TPLPs, and show that the xpoint theory may be used to enumerate consequences of a TPLP in a sound and ...

متن کامل

Probabilistic Description Logic Programs

Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertainty in the Rules, Logic, and Proof layers of the Semantic Web, we present probabilistic description logic programs (or pdl-programs), which are a combination of description logic programs (or dl-programs) under the answer set semantics and the well-founded semantics with Poole’s independent choice...

متن کامل

Revisiting the Semantics of Interval Probabilistic Logic Programs

Two approaches to logic programming with probabilities emerged over time: bayesian reasoning and probabilistic satisfiability (PSAT). The attrac­ tiveness of the former is in tying the logic programming research to the body of work on Bayes networks. The second approach ties computationally reasoning about probabilities with linear programming, and allows for natural expression of imprecision i...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Computer Science and Cybernetics

سال: 2016

ISSN: 1813-9663,1813-9663

DOI: 10.15625/1813-9663/10/3/8193