Logic Programs with Abstract Constraint Atoms: The Role of Computations

Liu, Lengning; Pontelli, Enrico; Son, Tran Cao; nacute, Mirosław Truszczy; ski,

doi:10.1007/978-3-540-74610-2_20

Cited by 37 publications

(67 citation statements)

References 18 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it was recently observed that for general logic programs, the FLP answer set semantics may produce answer sets with circular justifications that are caused by self-supporting loops [58,45]. The following two examples well illustrate this behavior.…”

Section: Introductionmentioning

confidence: 85%

“…Shen and You [57] gave an alternative characterization of the conditional satisfaction-based semantics in terms of a generalized Gelfond-Lifschitz transformation. Liu et al [45] proposed a computation-based answer set semantics for normal logic programs with c-atoms, which proves to coincide with the conditional satisfaction-based semantics.…”

Section: Related Workmentioning

confidence: 93%

See 1 more Smart Citation

FLP answer set semantics without circular justifications for general logic programs

Shen

Wang

Eiter

et al. 2014

Artificial Intelligence

View full text Add to dashboard Cite

The answer set semantics presented by Faber et al. [27] has been widely used to define so called FLP answer sets for different types of logic programs. However, it was recently observed that when being extended from normal to more general classes of logic programs, this approach may produce answer sets with circular justifications that are caused by selfsupporting loops. The main reason for this behavior is that the FLP answer set semantics is not fully constructive by a bottom up construction of answer sets. In this paper, we overcome this problem by enhancing the FLP answer set semantics with a level mapping formalism such that every answer set I can be built by fixpoint iteration of a one-step provability operator (more precisely, an extended van Emden-Kowalski operator for the FLP reduct f Π I ). This is inspired by the fact that under the standard answer set semantics, each answer set I of a normal logic program Π is obtainable by fixpoint iteration of the standard van Emden-Kowalski one-step provability operator for the Gelfond-Lifschitz reduct Π I , which induces a level mapping. The enhanced FLP answer sets, which we call well-justified FLP answer sets, are thanks to the level mapping free of circular justifications. As a general framework, the well-justified FLP answer set semantics applies to logic programs with first-order formulas, logic programs with aggregates, description logic programs, hex-programs etc., provided that the rule satisfaction is properly extended to such general logic programs. We study in depth the computational complexity of FLP and well-justified FLP answer sets for general classes of logic programs. Our results show that the level mapping does not increase the worst-case complexity of FLP answer sets. Furthermore, we describe an implementation of the well-justified FLP answer set semantics, and report about an experimental evaluation, which indicates a potential for performance improvements by the level mapping in practice.

show abstract

Section: Introductionmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 93%

FLP answer set semantics without circular justifications for general logic programs

Shen

Wang

Eiter

et al. 2014

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…ASPeRiX [12] has been concurrently and independently developed. Both GASP and ASPeRiX have their theoretical roots in the same notion of computation-based characterization of answer sets [16]. ASPeRiX is implemented in C++ and develops heuristics aimed at enhancing the choice of the rules when more of them are applicable.…”

Section: Related Workmentioning

confidence: 99%

“…The work described in [16] provides a computation-based characterization of answer sets for programs with abstract constraints. One of the outcomes of that research is the development of a computationbased view of answer sets for logic programs.…”

Section: Computations and Answer Setsmentioning

confidence: 99%

“…The characterization of answer sets derived from [16] does not require the initial guessing of a complete interpretation; instead it combines the guessing process with the construction of the answer set. The notion of computation characterizes answer sets through an incremental construction process, where the choices are performed at the level of what rules are actually applied to extend the partial answer set.…”

Section: Computations and Answer Setsmentioning

confidence: 99%

See 1 more Smart Citation

GASP: Answer Set Programming with Lazy Grounding

Palù

Dovier

Pontelli

et al. 2009

Fundamenta Informaticae

Self Cite

View full text Add to dashboard Cite

In recent years, Answer Set Programming has gained popularity as a viable paradigm for applications in knowledge representation and reasoning. This paper presents a novel methodology to compute answer sets of an answer set program. The proposed methodology maintains a bottom-up approach to the computation of answer sets (as in existing systems), but it makes use of a novel structuring of the computation, that originates from the non-ground version of the program. Grounding is lazily performed during the computation of the answer sets. The implementation has been realized using Constraint Logic Programming over finite domains.

show abstract

Techniques for Efficient Lazy-Grounding ASP Solving

Leutgeb

Weinzierl

2018

Declarative Programming and Knowledge Management

View full text Add to dashboard Cite

Logic Programs with Abstract Constraint Atoms: The Role of Computations

Cited by 37 publications

References 18 publications

FLP answer set semantics without circular justifications for general logic programs

FLP answer set semantics without circular justifications for general logic programs

GASP: Answer Set Programming with Lazy Grounding

Techniques for Efficient Lazy-Grounding ASP Solving

Contact Info

Product

Resources

About