Exploiting Justifications for Lazy Grounding of Answer Set Programs

Bogaerts, Bart; Weinzierl, Antonius

doi:10.24963/ijcai.2018/240

Cited by 13 publications

(17 citation statements)

References 2 publications

(3 reference statements)

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“…In the unfounded set algorithm (Gebser et al 2009), justifications for atoms are stored (the so-called source-pointer approach essentially maintains a justification). Bogaerts and Weinzierl (2018) used justifications to learn new clauses to improve search in lazy grounding algorithms. Additionally, justifications were used to improve parity game solvers (Lapauw et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Exploiting Game Theory for Analysing Justifications

Marynissen

Bogaerts

Denecker

2020

Theory and Practice of Logic Programming

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Justification theory is a unifying semantic framework. While it has its roots in non-monotonic logics, it can be applied to various areas in computer science, especially in explainable reasoning; its most central concept is a justification: an explanation why a property holds (or does not hold) in a model.In this paper, we continue the study of justification theory by means of three major contributions. The first is studying the relation between justification theory and game theory. We show that justification frameworks can be seen as a special type of games. The established connection provides the theoretical foundations for our next two contributions. The second contribution is studying under which condition two different dialects of justification theory (graphs as explanations vs trees as explanations) coincide. The third contribution is establishing a precise criterion of when a semantics induced by justification theory yields consistent results. In the past proving that such semantics were consistent took cumbersome and elaborate proofs.We show that these criteria are indeed satisfied for all common semantics of logic programming.

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Section: Introductionmentioning

confidence: 99%

Exploiting Game Theory for Analysing Justifications

Marynissen

Bogaerts

Denecker

2020

Theory and Practice of Logic Programming

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“…Almost none of the problem instances used in our experiments can be solved by ALPHA without domain-specific heuristics or by CLINGO when some techniques that are not supported by ALPHA are switched off. 6 Both HRP [19] and PUP [3,46] are abstracted versions of industrial (re)configuration problems. For problem definitions, we refer to the original sources.…”

Section: Resultsmentioning

confidence: 99%

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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“…These solvers all have in common that they process only normal rules, i.e., they allow for only ordinary literals in rule bodies and no disjunction in heads, and in particular, they do not support aggregates. Recent developments around Alpha are on exploiting justifications (Bogaerts and Weinzierl 2018) as well as its use in evaluating external atoms (Eiter, Kaminski, and Weinzierl 2017). Furthermore, Alpha is the only lazy-grounding ASP system with efficient techniques for search (CDNL), hence it was taken as basis for this work.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming

Bomanson

Janhunen

Weinzierl

2019

AAAI

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Answer-Set Programming (ASP) is an expressive rule-based knowledge-representation formalism. Lazy grounding is a solving technique that avoids the well-known grounding bottleneck of traditional ASP evaluation but is restricted to normal rules, severely limiting its expressive power. In this work, we introduce a framework to handle aggregates by normalizing them on demand during lazy grounding, hence relieving the restrictions of lazy grounding significantly. We term our approach as lazy normalization and demonstrate its feasibility for different types of aggregates. Asymptotic behavior is analyzed and correctness of the presented lazy normalizations is shown. Benchmark results indicate that lazy normalization can bring up-to exponential gains in space and time as well as enable ASP to be used in new application areas.

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Exploiting Justifications for Lazy Grounding of Answer Set Programs

Cited by 13 publications

References 2 publications

Exploiting Game Theory for Analysing Justifications

Exploiting Game Theory for Analysing Justifications

Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP

Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming

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