Optimizing Answer Set Computation via Heuristic-Based Decomposition

Calimeri, Francesco; Perri, Simona; Zangari, Jessica

doi:10.1017/s1471068419000036

Cited by 12 publications

(12 citation statements)

References 37 publications

(58 reference statements)

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“…Among grounders, I -DLV [5] employs a heuristic-guided tree decomposition algorithm [7] aiming at optimizing the instantiation process. Roughly, I -DLV possibly decomposes input rules into multiple smaller ones according to the technique sketched in Section 2 on the basis of a formulas that estimates the cost of joining body literals.…”

Section: Ml-guided Tree Decomposition Rewritingmentioning

confidence: 99%

A Machine Learning guided Rewriting Approach for ASP Logic Programs

Mastria

Zangari

Perri

et al. 2020

Electron. Proc. Theor. Comput. Sci.

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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 general, for designing an ASP encoding that can be efficiently evaluated by an actual ASP system. A common way for trying to reduce the burden of manually tweaking an ASP program consists in automatically rewriting the input encoding according to suitable techniques, for producing alternative, yet semantically equivalent, ASP programs. However, rewriting does not always grant benefits in terms of performance; hence, proper means are needed for predicting their effects with this respect. In this paper we describe an approach based on Machine Learning (ML) to automatically decide whether to rewrite. In particular, given an ASP program and a set of input facts, our approach chooses whether and how to rewrite input rules based on a set of features measuring their structural properties and domain information. To this end, a Multilayer Perceptrons model has then been trained to guide the ASP grounder I -DLV on rewriting input rules. We report and discuss the results of an experimental evaluation over a prototypical implementation.

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Section: Ml-guided Tree Decomposition Rewritingmentioning

confidence: 99%

A Machine Learning guided Rewriting Approach for ASP Logic Programs

Mastria

Zangari

Perri

et al. 2020

Electron. Proc. Theor. Comput. Sci.

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“…In particular, idlv-claspdlv makes use of dlv (Leone et al 2006;Maratea et al 2008) for instances featuring a ground query; otherwise, it consists of the combination of the grounder idlv with clasp executed with the option --configuration=trendy. The idlv+-clasp-dlv system is a variant of the previous system that uses a heuristic-guided rewriting technique (Calimeri et al 2018) relying on hyper-tree decomposition, which aims to automatically replace long rules with sets of smaller ones that are possibly evaluated more efficiently.…”

Section: Unicalmentioning

confidence: 99%

The Seventh Answer Set Programming Competition: Design and Results

Gebser

Maratea

Ricca

2019

Theory and Practice of Logic Programming

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Answer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).

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“…The overgrounding strategy described above has been implemented by adapting the I -DLV grounder (Calimeri et al 2017;Calimeri et al 2019); the architecture of the resulting prototype, called I -DLV -incr, is depicted in Figure 1. The system behaves as a process staying alive and providing a service-oriented behaviour, waiting for requests.…”

Section: Prototype Structure and Experimental Evaluationmentioning

confidence: 99%

“…For an overview of grounding optimization techniques the reader can refer to(Gebser et al 2011;Calimeri et al 2017;Calimeri et al 2019). …”

mentioning

confidence: 99%

Incremental Answer Set Programming with Overgrounding

Calimeri¹,

Ianni²,

Pacenza³

et al. 2019

Theory and Practice of Logic Programming

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Repeated executions of reasoning tasks for varying inputs are necessary in many applicative settings, such as stream reasoning. In this context, we propose an incremental grounding approach for the answer set semantics. We focus on the possibility of generating incrementally larger ground logic programs equivalent to a given non-ground one; so calledovergrounded programscan be reused in combination with deliberately many different sets of inputs. Updating overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is repeated on a series of inputs similar to each other. Notably, the proposed approach works “under the hood”, relieving designers of logic programs from controlling technical aspects of grounding engines and answer set systems. In this work we present the theoretical basis of the proposed incremental grounding technique, we illustrate the consequent repeated evaluation strategy and report about our experiments.

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Optimizing Answer Set Computation via Heuristic-Based Decomposition

Cited by 12 publications

References 37 publications

A Machine Learning guided Rewriting Approach for ASP Logic Programs

A Machine Learning guided Rewriting Approach for ASP Logic Programs

The Seventh Answer Set Programming Competition: Design and Results

Incremental Answer Set Programming with Overgrounding

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