Probabilistic logic programming is a programming paradigm that combines logic programming with probabilities. Most approaches to probabilistic logic programming...

Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically...

Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. Probabilistic...

Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples...

probabilistic logic programming language that extends Prolog with probabilities. It minimally extends Prolog by adding the notion of a probabilistic fact...

Logic programming is a programming, database and knowledge representation paradigm based on formal logic. A logic program is a set of sentences in logical...

first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models...

(2014). Compiling probabilistic logic programs into sentential decision diagrams. In Proceedings Workshop on Probabilistic Logic Programming (PLP) (pp. 1-10)...

Retrieved 19 May 2016. De Raedt, Luc; Kimmig, Angelika (2015). "Probabilistic (logic) programming concepts". Machine Learning. 100 (1): 5–47. doi:10.1007/s10994-015-5494-z...

A probabilistic logic network (PLN) is a conceptual, mathematical and computational approach to uncertain inference. It was inspired by logic programming...

inductive programming, such as functional logic programming, constraint programming, probabilistic programming, abductive logic programming, modal logic, action...

inference engine to automate probabilistic reasoning—a kind of Prolog for probability instead of logic. Bayesian programming is a formal and concrete implementation...

computer programming: Computer programming – process that leads from an original formulation of a computing problem to executable computer programs. Programming...

Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. It is applicable...

most likely hypothesis that should be adopted. Subjective logic generalises probabilistic logic by including degrees of epistemic uncertainty in the input...

either by signaling a failure or failing to terminate. In some cases, probabilistic algorithms are the only practical means of solving a problem. In common...

Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the study of deductively valid inferences or logical...

There are also examples of probabilistic reasoners, including non-axiomatic reasoning systems, and probabilistic logic networks. Notable semantic reasoners...

set programming to the problem of product configuration. In 1999, the term "answer set programming" appeared for the first time in a book The Logic Programming...

The LogicBlox system is a commercial, declarative, incremental logic programming language and deductive database inspired by Datalog. The LogiQL programming...

Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively, based on abductive...

expressible in this language, more powerful language based on logic programming and fixpoint logic such as Datalog were studied. The theory also explores foundations...

and probabilistic knowledge. She has been awarded the prize for the best application paper at the International Conference on Logic Programming (ICLP)...

autoepistemic logic can express knowledge and lack of knowledge about facts. The stable model semantics, which is used to give a semantics to logic programming with...

variety of reversible device concepts, logic gates, electronic circuits, processor architectures, programming languages, and application algorithms have...

In computing, an arithmetic logic unit (ALU) is a combinational digital circuit that performs arithmetic and bitwise operations on integer binary numbers...

logical truth values yields a multi-valued logic, which forms the basis for fuzzy logic and probabilistic logic. In these interpretations, a value is interpreted...

(2013). Bayesian Programming (1 edition) Chapman and Hall/CRC. Daniel Roy (2015). "Probabilistic Programming". probabilistic-programming.org. Archived from...

(human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames...

frequently used as an assumption for further mathematical work. Proofs employ logic expressed in mathematical symbols, along with natural language which usually...