Blending Lazy-Grounding and CDNL Search for Answer-Set Solving

Weinzierl, Antonius

doi:10.1007/978-3-319-61660-5_17

Cited by 36 publications

(56 citation statements)

References 14 publications

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“…Dependency graph and solved predicates seems to be treated in a similar manner to that of ASPeRiX. Current version (Weinzierl 2013) uses must-be-true propagation and tries to introduce methods for conflict-driven learning of non-ground rules: when a constraint is violated, a new constraint is built by unfolding of rules whose firing contributes to the conflict. This learned constraint is then transformed into special rules so as to be used for propagation.…”

Section: Resultsmentioning

confidence: 99%

`ASPeRiX`, a first-order forward chaining approach for answer set computing

Lefèvre¹,

Béatrix²,

Stéphan³

et al. 2017

Theory and Practice of Logic Programming

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The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first order logic program with default negation. In a preliminary step this program with variables is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation.In this article, the project ASPeRiX is presented as a first order forward chaining approach for Answer Set Computing. This project was amongst the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. The methodology applies a forward chaining of first order rules that are grounded on the fly by means of previously produced atoms. Theoretical foundations of the approach are presented, the main algorithms of the ASP solver ASPeRiX are detailed and some experiments and comparisons with existing systems are provided.

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Section: Resultsmentioning

confidence: 99%

`ASPeRiX`, a first-order forward chaining approach for answer set computing

Lefèvre¹,

Béatrix²,

Stéphan³

et al. 2017

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…The latter then solves this propositional encoding. The associated blow-up in space leads to the so-called grounding bottleneck which is tackled by lazy grounding [37,47].…”

Section: Integration Into a Lazy-grounding Asp Solvermentioning

confidence: 99%

“…To overcome the grounding bottleneck, lazy-grounding ASP systems such as GASP [41], ASPERIX [34], OMIGA [9], or ALPHA [47] interleave grounding and solving in order to instantiate and store only relevant parts of the ground program in memory.…”

Section: Introductionmentioning

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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“…The notion of three-valued oracle functions has also been used to extend HEX with lazy-grounding techniques , where not only the evaluation of external atoms is postponed, but also the grounding of the HEX-program itself, by employing as backendsolver the lazy-grounding ASP solver ALPHA (Weinzierl, 2017).…”

Section: Related Workmentioning

confidence: 99%

Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access

Eiter

Kaminski

Redl

et al. 2018

jair

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Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEXprograms were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

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Blending Lazy-Grounding and CDNL Search for Answer-Set Solving

Cited by 36 publications

References 14 publications

`ASPeRiX`, a first-order forward chaining approach for answer set computing

`ASPeRiX`, a first-order forward chaining approach for answer set computing

Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP

Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access

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Blending Lazy-Grounding and CDNL Search for Answer-Set Solving

Cited by 36 publications

References 14 publications

ASPeRiX, a first-order forward chaining approach for answer set computing

ASPeRiX, a first-order forward chaining approach for answer set computing

Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP

Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access

Contact Info

Product

Resources

About

`ASPeRiX`, a first-order forward chaining approach for answer set computing

`ASPeRiX`, a first-order forward chaining approach for answer set computing