Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

Weinzierl, Antonius; Taupe, Richard; Friedrich, Gerhard

doi:10.1017/s1471068420000332

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…. , w n : ℓ n and different bounds w 0 , and the lazy-grounding ASP system ALPHA (Weinzierl et al 2020) features an incremental on-demand rewriting (Bomanson et al 2019) to aggregate-free rules.…”

Section: Discussionmentioning

confidence: 99%

Aggregate Semantics for Propositional Answer Set Programs

Alviano¹,

Faber²,

Gebser³

2021

Preprint

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Answer Set Programming (ASP) emerged in the late 1990ies as a paradigm for Knowledge Representation and Reasoning. The attractiveness of ASP builds on an expressive high-level modeling language along with the availability of powerful off-the-shelf solving systems. While the utility of incorporating aggregate expressions in the modeling language has been realized almost simultaneously with the inception of the first ASP solving systems, a general semantics of aggregates and its efficient implementation have been longstanding challenges. Aggregates have been proposed and widely used in database systems, and also in the deductive database language Datalog, which is one of the main precursors of ASP. The use of aggregates was, however, still restricted in Datalog (by either disallowing recursion or only allowing monotone aggregates), while several ways to integrate unrestricted aggregates evolved in the context of ASP. In this survey, we pick up at this point of development by presenting and comparing the main aggregate semantics that have been proposed for propositional ASP programs. We highlight crucial properties such as computational complexity and expressive power, and outline the capabilities and limitations of different approaches by illustrative examples.Under consideration in Theory and Practice of Logic Programming (TPLP)

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Section: Discussionmentioning

confidence: 99%

Aggregate Semantics for Propositional Answer Set Programs

Alviano¹,

Faber²,

Gebser³

2021

Preprint

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“…Lazy grounding systems perform the grounding of rules during the search for an answer set [8,34,36,41,45,46]. In these approaches, a rule is instantiated only when its body is satisfied, thus grounding is done only for rules that are used during the search.…”

Section: Related Workmentioning

confidence: 99%

“…In these approaches, a rule is instantiated only when its body is satisfied, thus grounding is done only for rules that are used during the search. The state-of-the-art lazy-grounding system ALPHA [45,46] combines lazy instantiation techniques with learning, conflict-based heuristics, restarts, phase saving, etc. A key difference between ALPHA and PROASP is that the first discovers the space of propositional atoms during search, whereas PROASP computes it at the beginning.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, lazy grounding techniques perform grounding during search, with the aim to prevent computing unnecessary instantiations. Despite showing promising initial results and continuously improving efficiency, the performance of lazy grounding techniques is still not on par with state-of-the-art systems [46]. Recent work demonstrated that the grounding bottleneck can be overcome by compiling subprograms in external propagators, that simulate the presence of their ground counterpart during the solving phase [15].…”

Section: Introductionmentioning

confidence: 99%

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Compilation of Tight ASP Programs

Dodaro,

Mazzotta,

Ricca

2023

Frontiers in Artificial Intelligence and Applications

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Answer Set Programming (ASP) is a well-known AI formalism. Traditional ASP systems, that follow the “ground&solve” approach, are intrinsically limited by the so-called grounding bottleneck. Basically, the grounding step (i.e., variable-elimination) can be computationally expensive, and even unfeasible in several cases of practical interest. Recent work demonstrated that the grounding bottleneck can be partially overcome by compiling in external propagators subprograms acting as constraints. In this paper a novel compilation technique is presented that can be applied to tight normal programs; thus, the class of ASP programs that can be compiled is extended beyond constraints. The approach is implemented in the new system PROASP. PROASP skips entirely the grounding phase and performs solving by injecting custom propagators in GLUCOSE. An experiment, conducted on grounding-intensive ASP benchmarks, shows that PROASP is capable of solving instances that are out of reach for state-of-the-art ASP systems.

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“…In the context of metaprogramming, these results have been used for space-efficient ASP evaluation [13]. The literature distinguishes different attempts to efficient grounding, ranging from classical approaches [1,27] numerical techniques [23] and lazy grounding [35], over ASP modulo theory, e.g., [2], and methods based on structural measures (treewidth) [4,10,7,30]. Treewidth has been considered for ELPs [22], also in practice, e.g., [5].…”

Section: Introductionmentioning

confidence: 99%

On the Structural Complexity of Grounding – Tackling the ASP Grounding Bottleneck via Epistemic Programs and Treewidth

Besin,

Hecher,

Woltran

2023

Frontiers in Artificial Intelligence and Applications

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Answer Set Programming is widely applied research area for knowledge representation and for solving industrial domains. One of the challenges of this formalism focuses on the so-called grounding bottleneck, which addresses the efficient replacement of first-order variables by means of domain values. Recently, there have been several works in this direction, ranging from lazy grounding, hybrid solving, over translational approaches. Inspired by a translation from non-ground normal programs to ground disjunctive programs, we attack the grounding bottleneck from a more general angle. We provide a polynomial reduction for grounding disjunctive programs of bounded domain size by reducing to propositional epistemic logic programs (ELPs). By slightly adapting our reduction, we show new complexity results for non-ground programs that adhere to the measure treewidth. We complement these results by matching lower bounds under the exponential time hypothesis, ruling out significantly better algorithms.

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Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

Cited by 11 publications

References 35 publications

Aggregate Semantics for Propositional Answer Set Programs

Aggregate Semantics for Propositional Answer Set Programs

Compilation of Tight ASP Programs

On the Structural Complexity of Grounding – Tackling the ASP Grounding Bottleneck via Epistemic Programs and Treewidth

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