The ASP System DLV: Advancements and Applications

Adrian, Weronika T.; Alviano, Mario; Calimeri, Francesco; Cuteri, Bernardo; Dodaro, Carmine; Faber, Wolfgang; Fuscà, Davide; Leone, Nicola; Manna, Marco; Perri, Simona; Ricca, Filippo; Veltri, Pierfrancesco; Zangari, Jessica

doi:10.1007/s13218-018-0533-0

Cited by 25 publications

(19 citation statements)

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“…In particular, the formula for computing the assortativity degree of a graph G is defined as (Newman 2002 The formulas above, especially the one for assortativity, show that the computation of graph metrics can be in general not easy to be carried out using only rules in an ASP program. As a consequence, we resort to recent extensions of ASP systems, such as I-DLV/DLV2 (Calimeri et al 2017b;Adrian et al 2018), DLVHEX Calimeri et al 2016;, which allow the integration of external computa- tion sources within the ASP program. In particular, the problem at hand requires to send a (possibly guessed) entire graph, that is, a set of edges, to an external source of computation.…”

Section: Specialization For Studying Graph Metricsmentioning

confidence: 99%

“…In particular, the problem at hand requires to send a (possibly guessed) entire graph, that is, a set of edges, to an external source of computation. The ASP standardization group has not released standard language directives yet for such features; here, we make use of syntax and semantics of DLVHEX , while a slightly different formulation must be used to comply with I-DLV/DLV2 (Calimeri et al 2017b;Adrian et al 2018) or clingo ) syntax. Figure 6 shows the specialization of the Brain Evolution Simulation module for deriving the minimal changes to perform on a graph G i in order to obtain a decrease in the measure of a certain property (by 10% by edge removal in the example).…”

Section: Specialization For Studying Graph Metricsmentioning

confidence: 99%

“…The use of ASP for optimization problems is still relatively less popular than its use for decision problems, even though it has been proved to be perfectly suited for them; moreover, the formulas for the computation of graph metrics can be in general not easy to be written by rules only. This increases the interest in the application of recent extensions of ASP systems, such as I-DLV/DLV2 (Calimeri et al 2017b;Adrian et al 2018), DLVHEX Calimeri et al 2016;, etc., which allow both to solve optimization problems and the integration of external computation sources within the ASP program. The potentialities of ASP in these two areas are pointed out in the rest of the paper.…”

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confidence: 99%

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A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

Calimeri¹,

Cauteruccio²,

Cinelli³

et al. 2019

Theory and Practice of Logic Programming

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Deductive formalisms have been strongly developed in recent years; among them, answer set programming (ASP) gained some momentum and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to artificial intelligence; indeed, in several contexts, other approaches result to be more useful. Typical bioinformatics tasks, for instance classification, are currently carried out mostly by machine learning (ML)-based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians Theory and Practice of Logic Programming 81 and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.

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Section: Specialization For Studying Graph Metricsmentioning

confidence: 99%

Section: Specialization For Studying Graph Metricsmentioning

confidence: 99%

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confidence: 99%

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A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

Calimeri¹,

Cauteruccio²,

Cinelli³

et al. 2019

Theory and Practice of Logic Programming

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“…Answer set programming (ASP) [8], based on the logic programming paradigm, is an elegant method to represent defaults, exceptions and preferences. There are comprehensive, well known, implementations of ASP such as CLINGO [7] and DLV [1]. But most of them are based on grounding the logic program and then using a SAT solver to compute a model.…”

Section: Introductionmentioning

confidence: 99%

Conversational AI : Open Domain Question Answering and Commonsense Reasoning

Basu¹

2019

Electron. Proc. Theor. Comput. Sci.

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Our research is focused on making a human-like question answering system which can answer rationally. The distinguishing characteristic of our approach is that it will use automated common sense reasoning to truly "understand" dialogues, allowing it to converse like a human. Humans often make many assumptions during conversations. We infer facts not told explicitly by using our common sense. Incorporating commonsense knowledge in a question answering system will simply make it more robust.

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“…The Answer Set Programming (ASP) language has become very popular in the last years thanks to the availability of more and more efficient solvers (e.g., Clingo [8] and DLV [1]). It is based on the stable model semantics from Gelfond and Lifschitz [9], introduced to resemble the human reasoning process; together with its simple syntax, this makes ASP a very intuitive language to be used.…”

Section: Introduction and Problem Descriptionmentioning

confidence: 99%

Distributed Answer Set Coloring: Stable Models Computation via Graph Coloring

Bortoli

2019

Electron. Proc. Theor. Comput. Sci.

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Answer Set Programming (ASP) is a famous logic language for knowledge representation, which has been really successful in the last years, as witnessed by the great interest into the development of efficient solvers for ASP. Yet, the great request of resources for certain types of problems, as the planning ones, still constitutes a big limitation for problem solving. Particularly, in the case the program is grounded before the resolving phase, an exponential blow up of the grounding can generate a huge ground file, infeasible for single machines with limited resources, thus preventing even the discovering of a single non-optimal solution. To address this problem, in this paper we present a distributed approach to ASP solving, exploiting distributed computation benefits in order to overcome the just explained limitations. The here presented tool, which is called Distributed Answer Set Coloring (DASC), is a pure solver based on the well-known Graph Coloring algorithm. DASC is part of a bigger project aiming to bring logic programming into a distributed system, started in 2017 by Federico Igne with mASPreduce and continued in 2018 by Pietro Totis with a distributed grounder. In this paper we present a low level implementation of the Graph Coloring algorithm, via the Boost and MPI libraries for C++. Finally, we provide a few results of the very first working version of our tool, at the moment without any strong optimization or heuristic.I want to thank Fabio Tardivo, Agostino Dovier and Enrico Pontelli for their support during the development of the tool.

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The ASP System DLV: Advancements and Applications

Cited by 25 publications

References 5 publications

A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

Conversational AI : Open Domain Question Answering and Commonsense Reasoning

Distributed Answer Set Coloring: Stable Models Computation via Graph Coloring

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