Optimization Methods for the Partner Units Problem

Aschinger, Markus; Drescher, Conrad; Friedrich, Gerhard; Gottlob, Georg; Jeavons, Peter; Ryabokon, Anna; Thorstensen, Evgenij

doi:10.1007/978-3-642-21311-3_4

Cited by 34 publications

(65 citation statements)

References 11 publications

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“…On the Partner Units Problem, another challenging configuration problem, we reach the same performance as the answer set program presented in [2] if for the latter the problem-specific search strategy is turned off.…”

Section: Implementing Locosupporting

confidence: 49%

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LoCo—A Logic for Configuration Problems

Aschinger

Drescher

Gottlob

et al. 2014

ACM Trans. Comput. Logic

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Abstract. LoCo is a fragment of classical first order logic tailored for expressing configuration problems. The core feature of LoCo is that the number of components used in configurations does not have to be finitely bounded explicitly, but instead is bounded implicitly through the axioms. Computing configurations reduces to modelfinding. We present the language, related algorithms and complexity results as well as a prototypical implementation via answer set programming.

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Section: Implementing Locosupporting

confidence: 49%

“…When l = u in the counting quantifier we can address each component instance and the respective attributes individually, abbreviated here as e.g. big [1] and big [2]. While the order of the instances is not defined there clearly exists a permutation such that the constraint is satisfied.…”

Section: The Language Of Locomentioning

confidence: 99%

LoCo—A Logic for Configuration Problems

Aschinger

Drescher

Gottlob

et al. 2014

ACM Trans. Comput. Logic

Self Cite

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“…Such conditions are addressed by side (Balduccini, 2011) deals with assigning jobs to devices such that the makespan of a schedule stays within a given budget. The matching problem Partner Units (Aschinger et al, 2011) has applications in the configuration of surveillance, electrical engineering, computer network, and railway safety systems. Crossing Minimization (Gange, Stuckey, & Marriott, 2010) aims at optimized layouts of hierarchical network diagrams in graph drawing.…”

Section: Previous Domainsmentioning

confidence: 99%

The Sixth Answer Set Programming Competition

Gebser¹,

Maratea²,

Ricca³

2017

jair

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Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely related problemsolving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advancements in ASP solving are assessed in competition events. In this paper, we report about the design and results of the Sixth ASP Competition, which was jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genoa (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning. This edition maintained some of the design decisions introduced in 2014, e.g., the conception of sub-tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. On the other hand, it featured also some novelties, like a benchmark selection stage classifying instances according to their empirical hardness, and a "Marathon" track where the topperforming systems are given more time for solving hard benchmarks.

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“…When UCAP and IUCAP are not part of the input and thus are limited by some fixed natural number, the situation is two-sided. Whereas for the special case with IUCAP=2 a polynomial-time algorithm could be found, algorithmic complexity remains unclear for the problem classes where IUCAP≥3 [1] and as a consequence no polynomial-time algorithm is known. Furthermore, also the polynomial-time algorithm presented in [2] does not scale well.…”

Section: Proof Of Concept Evaluation a The Benchmark Problemmentioning

confidence: 99%

Declarative Heuristics in Constraint Satisfaction

Teppan

Friedrich

2013

2013 IEEE 25th International Conference on Tools With Artificial Intelligence

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Constraint Satisfaction Problems (CSPs) have the big advantage of a succinct, declarative and easy to understand representation form. Unfortunately, solving CSPs is NPcomplete in the general case. In order to cope with this, common CSP frameworks offer the possibility to use different built-in heuristics. However, the provided built-in heuristics are often not suitable to significantly boost solution calculation. Also the facilities for expressing domain-specific heuristics in a declarative manner within the CSP framework are typically very limited (e.g. by defining a static variable selection order) and thus are often not applicable. As a consequence such domain-specific heuristics are often implemented by means of custom propagators or custom constraints (e.g. a special constraint for bin packing problems) forcing domain experts and knowledge engineers to leave the declarative world and implement the heuristics in a procedural manner. In this paper we propose a new declarative language for expressing domain specific heuristics for CSPs which can be easily integrated in every CSP framework. We also describe a prototype implementation within a state-of-the-art CSP solver and present proof of concept results on real world configuration problem instances.

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Optimization Methods for the Partner Units Problem

Cited by 34 publications

References 11 publications

LoCo—A Logic for Configuration Problems

LoCo—A Logic for Configuration Problems

The Sixth Answer Set Programming Competition

Declarative Heuristics in Constraint Satisfaction

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