182 research outputs found
Bialgebraic Semantics for Logic Programming
Bialgebrae provide an abstract framework encompassing the semantics of
different kinds of computational models. In this paper we propose a bialgebraic
approach to the semantics of logic programming. Our methodology is to study
logic programs as reactive systems and exploit abstract techniques developed in
that setting. First we use saturation to model the operational semantics of
logic programs as coalgebrae on presheaves. Then, we make explicit the
underlying algebraic structure by using bialgebrae on presheaves. The resulting
semantics turns out to be compositional with respect to conjunction and term
substitution. Also, it encodes a parallel model of computation, whose soundness
is guaranteed by a built-in notion of synchronisation between different
threads
Exploiting parallelism in coalgebraic logic programming
We present a parallel implementation of Coalgebraic Logic Programming (CoALP)
in the programming language Go. CoALP was initially introduced to reflect
coalgebraic semantics of logic programming, with coalgebraic derivation
algorithm featuring both corecursion and parallelism. Here, we discuss how the
coalgebraic semantics influenced our parallel implementation of 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
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
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
Operational Semantics of Resolution and Productivity in Horn Clause Logic
This paper presents a study of operational and type-theoretic properties of
different resolution strategies in Horn clause logic. We distinguish four
different kinds of resolution: resolution by unification (SLD-resolution),
resolution by term-matching, the recently introduced structural resolution, and
partial (or lazy) resolution. We express them all uniformly as abstract
reduction systems, which allows us to undertake a thorough comparative analysis
of their properties. To match this small-step semantics, we propose to take
Howard's System H as a type-theoretic semantic counterpart. Using System H, we
interpret Horn formulas as types, and a derivation for a given formula as the
proof term inhabiting the type given by the formula. We prove soundness of
these abstract reduction systems relative to System H, and we show completeness
of SLD-resolution and structural resolution relative to System H. We identify
conditions under which structural resolution is operationally equivalent to
SLD-resolution. We show correspondence between term-matching resolution for
Horn clause programs without existential variables and term rewriting.Comment: Journal Formal Aspect of Computing, 201
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