6,120 research outputs found
A Coalgebraic Perspective on Probabilistic Logic Programming
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper proposes a coalgebraic perspective on probabilistic logic programming. Programs are modelled as coalgebras for a certain functor F, and two semantics are given in terms of cofree coalgebras. First, the cofree F-coalgebra yields a semantics in terms of derivation trees. Second, by embedding F into another type G, as cofree G-coalgebra we obtain a "possible worlds" interpretation of programs, from which one may recover the usual distribution semantics of probabilistic logic programming
A coalgebraic perspective on probabilistic logic programming
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper proposes a coalgebraic perspective on probabilistic logic programming. Programs are modelled as coalgebras for a certain functor F, and two semantics are given in terms of cofree coalgebras. First, the cofree F-coalgebra yields a semantics in terms of derivation trees. Second, by embedding F into another type G, as cofree G-coalgebra we obtain a “possible worlds” interpretation of programs, from which one may recover the usual distribution semantics of probabilistic logic programming
Coalgebraic semantics for probabilistic logic programming
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper proposes a coalgebraic semantics on probabilistic logic programming. Programs are modelled as coalgebras for a certain functor F, and two semantics are given in terms of cofree coalgebras. First, the cofree F-coalgebra yields a semantics in terms of derivation trees. Second, by embedding F into another type G, as cofree G-coalgebra we obtain a 'possible worlds' interpretation of programs, from which one may recover the usual distribution semantics of probabilistic logic programming. Furthermore, we show that a similar approach can be used to provide a coalgebraic semantics to weighted logic programming
Coalgebraic Semantics for Probabilistic Logic Programming
Probabilistic logic programming is increasingly important in artificial
intelligence and related fields as a formalism to reason about uncertainty. It
generalises logic programming with the possibility of annotating clauses with
probabilities. This paper proposes a coalgebraic semantics on probabilistic
logic programming. Programs are modelled as coalgebras for a certain functor F,
and two semantics are given in terms of cofree coalgebras. First, the
F-coalgebra yields a semantics in terms of derivation trees. Second, by
embedding F into another type G, as cofree G-coalgebra we obtain a `possible
worlds' interpretation of programs, from which one may recover the usual
distribution semantics of probabilistic logic programming. Furthermore, we show
that a similar approach can be used to provide a coalgebraic semantics to
weighted logic programming
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
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