Answer Set Programming by Ant Colony Optimization

Pichon, Nicolas; Saubion, Frédéric; Stéphan, Igor

doi:10.1007/3-540-45757-7_40

Cited by 3 publications

(6 citation statements)

References 11 publications

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“…Both Smodels and ASSAT are specialized for problems in NP, and we are aware that both are rather efficient on several NP-complete problems. In the literature, benchmark results on Hamiltonian Path or Hamiltonian Circuit have been reported also in [Lin and Zhao 2002] and [Nicolas et al 2002]. While the findings in [Nicolas et al 2002] appear to confirm our results, those in [Lin and Zhao 2002] are quite different.…”

Section: Resultssupporting

confidence: 88%

“…In the literature, benchmark results on Hamiltonian Path or Hamiltonian Circuit have been reported also in [Lin and Zhao 2002] and [Nicolas et al 2002]. While the findings in [Nicolas et al 2002] appear to confirm our results, those in [Lin and Zhao 2002] are quite different. Apparently, this difference is mainly due to the encodings employed in the benchmarks.…”

Section: Resultssupporting

confidence: 88%

“…Our experimental results are complemented by other efforts on benchmarking ASP systems recently reported in the literature [Dix et al 2002;Koch and Leone 1999;Lin and Zhao 2002;Nicolas et al 2002;Simons et al 2002;Eiter et al 1998a;Janhunen et al 2003]. In general, these confirm our claim that DLV handles deductive database applications and P 2 -complete problems more efficiently than the other systems, while the situation is less clear on NP-complete problems where the results depend, to a larger extent, on the encodings adopted.…”

Section: Discussionsupporting

confidence: 92%

“…In general, these confirm our claim that DLV handles deductive database applications and Σ P 2 -complete problems more efficiently than the other systems, while the situation is less clear on NP-complete problems where the results depend, to a larger extent, on the encodings adopted. While the experiments in [Koch and Leone 1999;Lin and Zhao 2002;Nicolas et al 2002;Simons et al 2002;Eiter et al 1998a] are conducted on benchmark problems very similar to those used in this paper, a completely different approach has been taken in [Dix et al 2002]. This paper reports on a most recent comprehensive benchmarking activity which was independently performed in the context of Hierarchical Task Network (HTN) Planning based on ASP.…”

Section: Resultsmentioning

confidence: 99%

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The DLV system for knowledge representation and reasoning

Leone

Pfeifer

Faber

et al. 2006

ACM Trans. Comput. Logic

835

725

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Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows one to express every property of finite structures that is decidable in the complexity class P 2 ( NP NP ). Thus, under widely believed assumptions, DLP is strictly more expressive than normal (disjunction-free) logic programming, whose expressiveness is limited to properties decidable in NP. Importantly, apart from enlarging the class of applications which can be encoded in the language, disjunction often allows for representing problems of lower complexity in a simpler and more natural fashion.This article presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to P 3 -complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of DLV, and by deriving new complexity results we chart a complete picture of the complexity of this language and important fragments thereof.Furthermore, we illustrate the general architecture of the DLV system, which has been influenced by these results. As for applications, we overview application front-ends which have been 500 • N. Leone et al.developed on top of DLV to solve specific knowledge representation tasks, and we briefly describe the main international projects investigating the potential of the system for industrial exploitation. Finally, we report about thorough experimentation and benchmarking, which has been carried out to assess the efficiency of the system. The experimental results confirm the solidity of DLV and highlight its potential for emerging application areas like knowledge management and information integration.

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Section: Resultssupporting

confidence: 88%

Section: Resultssupporting

confidence: 88%

Section: Discussionsupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

The DLV system for knowledge representation and reasoning

Leone

Pfeifer

Faber

et al. 2006

ACM Trans. Comput. Logic

835

725

View full text Add to dashboard Cite

show abstract

“…Even those solvers which are capable of using incomplete (but potentially faster) search algorithms will fall back to a complete search algorithm when the heuristic method flounders [18]. Genetic algorithms have been proposed for this role [19], and ant colony optimization has been demonstrated as a core answer set solving algorithm in small-scale examples [20].…”

Section: Asp Solversmentioning

confidence: 99%

Answer Set Programming for Procedural Content Generation: A Design Space Approach

Smith

Mateas

2011

IEEE Trans. Comput. Intell. AI Games

127

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Abstract-Procedural content generators for games produce artifacts from a latent design space. This space is often only implicitly defined, an emergent result of the procedures used in the generator. In this paper, we outline an approach to content generation that centers on explicit description of the design space, using domainindependent procedures to produce artifacts from the described space. By concisely capturing a design space as an answer set program, we can rapidly define and expressively sculpt new generators for a variety of game content domains. We walk through the reimplementation of a reference evolutionary content generator in a tutorial example, and review existing applications of answer set programming to generative-content design problems in and outside of a game context. Index Terms-Answer set programming, constraint programming, game design, logic programming, procedural content generation.

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CatSAT: A Practical, Embedded, SAT Language for Runtime PCG

Horswill

2018

AIIDE

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Answer-set programming (ASP), a family of SAT-based logic programming systems, is attractive for procedural content generation. Unfortunately, current solvers present significant barriers to runtime use in games. In this paper, I discuss some of the issues involved, and present CatSAT, a solver designed to better fit the run-time resource constraints of modern games. Although intended only for small problems, it allows designers to compactly specify simple PCG problems such as NPC generation, solve them in a few tens of microseconds, and to adapt solutions dynamically based on the changing needs of gameplay. We hope that by making adoption as convenient as possible, we can increase the uptake of declarative techniques among developers.

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Answer Set Programming by Ant Colony Optimization

Cited by 3 publications

References 11 publications

The DLV system for knowledge representation and reasoning

The DLV system for knowledge representation and reasoning

Answer Set Programming for Procedural Content Generation: A Design Space Approach

CatSAT: A Practical, Embedded, SAT Language for Runtime PCG

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