Concolic testing mixes symbolic and concrete execution to generate test cases covering paths effectively. Its benefits have been demonstrated for more than 15 years to test imperative programs. Other programming parad...
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We present xclingo, a tool for generating explanations from ASP programs annotated with text and labels. These annotations allow tracing the application of rules or the atoms derived by them. The input of xclingo is a...
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Recently, some researchers [9, 1, 2] introduced the notions of subjective constraint monotonicity, epistemic splitting, and foundedness for epistemic logic programs, aiming to use them as main criteria/intuitions to c...
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In the context of multiple, repeated, execution of reasoning tasks, typical of stream reasoning and other applicative settings, we propose an incremental reasoning infrastructure, based on the answer set semantics. We...
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ion is a well-known approach to simplify a complex problem by over-approximating it with a deliberate loss of information. It was not considered so far in Answer Set programming (ASP), a convenient tool for problem so...
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We report on work in progress aiming to add blame features to property-based-testing in logic programming, in particular w.r.t. the mechanized meta-theory model checker αCheck. Once the latter reports a counterexampl...
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We develop formal foundations for notions and mechanisms needed to support service-oriented computing. Our work provides semantics for the service overlay by abstracting concepts from logic programming. It draws a str...
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Answer Set programming (ASP) is a declarative logic formalism that allows to encode computational problems via logic programs. Despite the declarative nature of the formalism, some advanced expertise is required, in g...
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AdSiF (Agent driven Simulation Framework) provides a programming environment for modeling, simulation, and programming agents, which fuses agent-based, object-oriented, aspect-oriented, and logic programming into a si...
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AdSiF (Agent driven Simulation Framework) provides a programming environment for modeling, simulation, and programming agents, which fuses agent-based, object-oriented, aspect-oriented, and logic programming into a single paradigm. The power of this paradigm stems from its ontological background and the paradigms it embraces and integrates into a single paradigm called state-oriented programming. AdSiF commits to describe what exists and to model the agent reasoning abilities, which thereby drives model behaviors. Basically, AdSiF provides a knowledgebase and a depth first search mechanism for reasoning. It is possible to model different search mechanism for reasoning but depth first search is a default search mechanism for first order reasoning. The knowledge base consists of facts and predicates. The reasoning mechanism is combined with a dual-world representation, it is defined as an inner representation of a simulated environment, and it is constructed from time-stamped sensory data (or beliefs) obtained from that environment even when these data consist of errors. This mechanism allows the models to make decisions using the historical data of the models and its own states. The study provides a novel view to simulation and agent-modeling using a script-based graph programming structuring state-oriented programming with a multi-paradigm approach. The study also enhances simulation modeling and agent programming using logic programming and aspect orientation. It provides a solution framework for continuous and discrete event simulation and allows modelers to use their own simulation time management, event handling, distributed, and real time simulation algorithms. (C) 2018 Elsevier B.V. All rights reserved.
PRISM is a probabilistic programming language based on Prolog, augmented with primitives to represent probabilistic choice. It is implemented using a combination of low level support from a modified version of B-Prolo...
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PRISM is a probabilistic programming language based on Prolog, augmented with primitives to represent probabilistic choice. It is implemented using a combination of low level support from a modified version of B-Prolog, source level program transformation, and libraries for inference and learning implemented in C. More recently, developers working with functional programming languages have taken the approach of embedding probabilistic primitives into an existing language, with little or no modification to the host language, often by using delimited continuations. Captured continuations represent pieces of the probabilistic program which can be manipulated to achieve a great variety of computational effects useful for inference. In this paper, I will describe an approach based on delimited control operators recently introduced into SWI Prolog. These are used to create a system of nested effect handlers which together implement a core functionality of PRISM the building of explanation graphs entirely in Prolog and using an order of magnitude less code. Other declarative programming tools, such as constraint logic programming, are used to implement tools for inference, such as the inside-outside and EM algorithms, lazy best-first explanation search, and MCMC samplers. By embedding the functionality of PRISM into SWI Prolog, users gain access to its rich libraries and development environment. By expressing the functionality of PRISM in a small amount of pure, high-level Prolog, this implementation facilitates further experimentation with the mechanisms of probabilistic logic programming, including new probabilistic modelling features and inference algorithms, such as variational inference in models with real-valued variables. (C) 2018 Elsevier Inc. All rights reserved.
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