The ASP System DLV2

Alviano, Mario; Calimeri, Francesco; Dodaro, Carmine; Fuscà, Davide; Leone, Nicola; Perri, Simona; Ricca, Francesco; Veltri, Pierfrancesco; Zangari, Jessica

doi:10.1007/978-3-319-61660-5_19

Cited by 67 publications

(53 citation statements)

References 12 publications

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“…The JVM was called with command-line parameters -Xms1G -Xmx32G. For comparison, CLINGO [22] was used in version 5.3.0 and DLV2 [2] in version 2.0.…”

Section: Resultsmentioning

confidence: 99%

“…Randomness and restarts should also be supported, since such features are used by several real-world domain-specific heuristics. Since our approach has only been implemented in a lazy-grounding system so far, an adaption to ground-and-solve systems like CLINGO [22] or DLV2 [2] should be investigated.…”

Section: Discussionmentioning

confidence: 99%

“…The directive in which X has been substituted by 2 has the higher weight, however. For this reason, it is chosen, and the solver finds the (in this case) only rule that can make the heuristics' head b(2) true: b(2) ← x(2), not c (2). The choice point representing the body of this rule is made true and, after some propagation, the new partial assignment will contain b(2) (amongst other consequences of propagation).…”

Section: Integration Into a Lazy-grounding Asp Solvermentioning

confidence: 99%

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Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP

Taupe

Schekotihin

Schüller

et al. 2019

Electron. Proc. Theor. Comput. Sci.

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Domain-specific heuristics are an important technique for solving combinatorial problems efficiently. We propose a novel semantics for declarative specifications of domain-specific heuristics in Answer Set Programming (ASP). Decision procedures that are based on a partial solution are a frequent ingredient of existing domain-specific heuristics, e.g., for placing an item that has not been placed yet in bin packing. Therefore, in our novel semantics negation as failure and aggregates in heuristic conditions are evaluated on a partial solver state. State-of-the-art solvers do not allow such a declarative specification. Our implementation in the lazy-grounding ASP system Alpha supports heuristic directives under this semantics. By that, we also provide the first implementation for incorporating declaratively specified domain-specific heuristics in a lazy-grounding setting. Experiments confirm that the combination of ASP solving with lazy grounding and our novel heuristics can be a vital ingredient for solving industrial-size problems.

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“…The JVM was called with command-line parameters -Xms1G -Xmx32G. For comparison, CLINGO [22] was used in version 5.3.0 and DLV2 [2] in version 2.0.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Integration Into a Lazy-grounding Asp Solvermentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP

Taupe

Schekotihin

Schüller

et al. 2019

Electron. Proc. Theor. Comput. Sci.

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“…For the extended running example, extended DT yields the following rules. (N1) (Steps 2,3, DT Step 1) d_p2_bb (1,2).…”

Section: Extended Demand Transformationmentioning

confidence: 99%

“…To confirm the effectiveness of our method, we implemented our method to perform extended DT, and then created a Python program that performs extended BOTTOM-UP on the resulting rules. We compare our results with state-of-the-art systems: clingo [12], an ASP solver, DLV2, the latest version of a deductive database with ASP and dynamic magic sets [3,2], and XSB [10], the dominant engine with tabling and well-founded semantics.…”

Section: Applications and Experiments 61 Experiments On The Extendedmentioning

confidence: 99%

Extended Magic for Negation: Efficient Demand-Driven Evaluation of Stratified Datalog with Precise Complexity Guarantees

Tekle

Liu

2019

Electron. Proc. Theor. Comput. Sci.

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Given a set of Datalog rules, facts, and a query, answers to the query can be inferred bottom-up starting from the facts or top-down starting from the query. For efficiency, top-down evaluation is extended with memoization of inferred facts, and bottom-up evaluation is performed after transformations to make rules driven by the demand from the query. Prior work has shown their precise complexity analysis and relationships. However, when Datalog is extended with even stratified negation, which has a simple and universally accepted semantics, transformations to make rules demanddriven may result in non-stratified negation, which has had many complex semantics and evaluation methods.This paper presents (1) a simple extension to demand transformation, a transformation to make rules demand-driven for Datalog without negation, to support stratified negation, and (2) a simple extension to an optimal bottom-up evaluation method for Datalog with stratified negation, to handle non-stratified negation in the resulting rules. We show that the method provides precise complexity guarantees. It is also optimal in that only facts needed for top-down evaluation of the query are inferred and each firing of a rule to infer such a fact takes worst-case constant time. We extend the precise relationship between top-down evaluation and demand-driven bottom-up evaluation to Datalog with stratified negation. Finally, we show experimental results for performance, as well as applications to previously challenging examples.

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