Efficient Groundness Analysis in Prolog

Boolean functions can be used to express the groundness of, and trace grounding dependencies between, program variables in (constraint) logic programs. In this paper, a variety of issues pertaining to the efficient Prolog implementation of groundness analysis are investigated, focusing on the domain of definite Boolean functions, Def. The systematic design of the representation of an abstract domain is discussed in relation to its impact on the algorithmic complexity of the domain operations; the most frequently called operations should be the most lightweight. This methodology is applied to Def, resulting in a new representation, together with new algorithms for its domain operations utilising previously unexploited properties of Def -- for instance, quadratic-time entailment checking. The iteration strategy driving the analysis is also discussed and a simple, but very effective, optimisation of induced magic is described. The analysis can be implemented straightforwardly in Prolog and the use of a non-ground representation results in an efficient, scalable tool which does not require widening to be invoked, even on the largest benchmarks. An extensive experimental evaluation is givenComment: 31 pages To appear in Theory and Practice of Logic Programmin

Bayesian Logic Programs

Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developedComment: 52 page

HoCHC: A Refutationally Complete and Semantically Invariant System of Higher-order Logic Modulo Theories

We present a simple resolution proof system for higher-order constrained Horn clauses (HoCHC) - a system of higher-order logic modulo theories - and prove its soundness and refutational completeness w.r.t. the standard semantics. As corollaries, we obtain the compactness theorem and semi-decidability of HoCHC for semi-decidable background theories, and we prove that HoCHC satisfies a canonical model property. Moreover a variant of the well-known translation from higher-order to 1st-order logic is shown to be sound and complete for HoCHC in standard semantics. We illustrate how to transfer decidability results for (fragments of) 1st-order logic modulo theories to our higher-order setting, using as example the Bernays-Schonfinkel-Ramsey fragment of HoCHC modulo a restricted form of Linear Integer Arithmetic

Categorical Modelling of Logic Programming: Coalgebra, Functorial Semantics, String Diagrams

Logic programming (LP) is driven by the idea that logic subsumes computation. Over the past 50 years, along with the emergence of numerous logic systems, LP has also grown into a large family, the members of which are designed to deal with various computation scenarios. Among them, we focus on two of the most influential quantitative variants are probabilistic logic programming (PLP) and weighted logic programming (WLP). In this thesis, we investigate a uniform understanding of logic programming and its quan- titative variants from the perspective of category theory. In particular, we explore both a coalgebraic and an algebraic understanding of LP, PLP and WLP. On the coalgebraic side, we propose a goal-directed strategy for calculating the probabilities and weights of atoms in PLP and WLP programs, respectively. We then develop a coalgebraic semantics for PLP and WLP, built on existing coalgebraic semantics for LP. By choosing the appropriate functors representing probabilistic and weighted computation, such coalgeraic semantics characterise exactly the goal-directed behaviour of PLP and WLP programs. On the algebraic side, we define a functorial semantics of LP, PLP, and WLP, such that they three share the same syntactic categories of string diagrams, and differ regarding to the semantic categories according to their data/computation type. This allows for a uniform diagrammatic expression for certain semantic constructs. Moreover, based on similar approaches to Bayesian networks, this provides a framework to formalise the connection between PLP and Bayesian networks. Furthermore, we prove a sound and complete aximatization of the semantic category for LP, in terms of string diagrams. Together with the diagrammatic presentation of the fixed point semantics, one obtain a decidable calculus for proving the equivalence between propositional definite logic programs

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