Showing papers on "Functional logic programming published in 2019"
Book•
29 Aug 2019
TL;DR: Answer set programming (ASP) as mentioned in this paper is a form of declarative programming oriented towards combinatorial search problems, which has been applied to plan generation and product configuration problems in artificial intelligence and to graph-theoretic problems arising in VLSI design.
Abstract: Answer set programming (ASP) is a form of declarative programming oriented towards difficult combinatorial search problems. It has been applied, for instance, to plan generation and product configuration problems in artificial intelligence and to graph-theoretic problems arising in VLSI design and in historical linguistics. Syntactically, ASP programs look like Prolog programs, but the computational mechanisms used in ASP are different: they are based on the ideas that have led to the development of fast satisfiability solvers for propositional logic. Answer set programming has emerged from interaction between two lines of research — on the semantics of negation in logic programming [Gelfond and Lifschitz, 1988] and on applications of satisfiability solvers to search problems [Kautz and Selman, 1992]. It was identified as a new programming paradigm in [Lifschitz, 1999; Marek and Truszczy´
74 citations
Book•
01 Jan 2019
TL;DR: Programming Interview Questions | GeekInterview.comStroustrup: Programming -Principles and Practice Using Online High School – Liberty University Online AcademyMcGraw Hill Medical Books McGraw-Hill Professional[PDF] Programming Books Collection Free Download
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16 citations
Dissertation•
13 Dec 2019
TL;DR: This thesis addresses limitations with existing narrowing-based testing tools by developing an approach to narrowing that is both practical and principled, and demonstrates how this can be used to expand the range of properties that can be automatically tested using a narrowing- based approach.
Abstract: Narrowing is one of the primary methods for implementing functional logic programming languages. Property-based testing is an automatic approach to assuring the correctness of software systems. In recent years, a number of systems have been developed that seek to apply the benefits of narrowing in the area of property-based testing. This thesis considers two limitations with these systems. First of all, most of the existing narrowing-based testing tools have focused on practical issues, and lack supporting theory. And secondly, these tools typically only perform well on properties that have particular forms. We address these limitations by developing an approach to narrowing that is both practical and principled, and demonstrate how this can be used to expand the range of properties that can be automatically tested using a narrowing-based approach.
1 citations
Posted Content•
TL;DR: It is shown that an implementation based on the concepts of functional logic programming can have benefits with respect to performance compared to a standard list-based implementation and can even compete with full-blown probabilistic programming languages, which is illustrated by several benchmarks.
Abstract: This paper presents PFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, language characteristics from one area exist in the other and vice versa. For example, the concepts of non-deterministic choice and call-time choice as known from functional logic programming are related to and coincide with stochastic memoization and probabilistic choice in probabilistic programming, respectively. We will further see that an implementation based on the concepts of functional logic programming can have benefits with respect to performance compared to a standard list-based implementation and can even compete with full-blown probabilistic programming languages, which we illustrate by several benchmarks. Under consideration in Theory and Practice of Logic Programming (TPLP).
1 citations