A Framework for Constraint Based Local Search using Essence

Akgün, Özgür; Attieh, Saad Wasim A; Gent, Ian P.; Jefferson, Christopher; Miguel, Ian; Nightingale, Peter; Salamon, András Z.; Spracklen, Patrick; Wetter, James

doi:10.24963/ijcai.2018/173

Cited by 6 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach was further proclaimed in [8] and implemented by the Yuck [12] and OscaR/CBLS [3] solvers, providing dedicated neighborhoods to handle specific global constraints. Recently, a system was proposed which infers plausible neighborhood operators from data structures occurring in the Constraint Programming model, as defined in the Essence language [1]. During the search, the solver switches between those promising neighborhoods using classic multi-arm bandit algorithms.…”

Section: Related Workmentioning

confidence: 99%

Generating Local Search Neighborhood with Synthesized Logic Programs

Ślażyński¹,

Abreu

Nalepa³

2019

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems. Their performance depends strongly on the search space landscape, as defined by a cost function and the selected neighborhood operators. In this paper we present a logic programming based framework, named Noodle, designed to generate bespoke Local Search neighborhoods tailored to specific discrete optimization problems. The proposed system consists of a domain specific language, which is inspired by logic programming, as well as a genetic programming solver, based on the grammar evolution algorithm. We complement the description with a preliminary experimental evaluation, where we synthesize efficient neighborhood operators for the traveling salesman problem, some of which reproduce well-known results.

show abstract

Section: Related Workmentioning

confidence: 99%

Generating Local Search Neighborhood with Synthesized Logic Programs

Ślażyński¹,

Abreu

Nalepa³

2019

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Recently, there has been a proposal for a system inferring possible neighborhood operators from data structures occurring in the Constraint Programming model, defined in the Essence language [3]. Various data structures are connected with relevant move operators that are tried in an intelligent manner using classic multi-arm bandit strategies.…”

Section: Background and Overview Of The Existing Literaturementioning

confidence: 99%

Research Report on Automatic Synthesis of Local Search Neighborhood Operators

Ślażyński

2019

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

Constraint Programming (CP) and Local Search (LS) are different paradigms for dealing with combinatorial search and optimization problems. Their complementary features motivated researchers to create hybrid CP/LS solutions, maintaining both the modeling capabilities of CP and the computational advantages of the heuristic-based LS approach. Research presented in this report is focused on developing a novel method to infer an efficient LS neighborhood operator based on the problem structure, as modeled in the CP paradigm. We consider a limited formal language that we call a Neighborhood Definition Language, used to specify the neighborhood operators in a fine-grained and declarative manner. Together with Logic Programming runtime called Noodle, it allows to automatically synthesize complex operators using a Grammar Evolution algorithm.

show abstract

“…Existing approaches typically accept as input models written in solver-independent modelling languages like MiniZinc [Nethercote et al, 2007]. The recently-proposed Structured Neighbourhood Search (SNS) [Akgün et al, 2018] differs in that it begins from a specification of a problem in the abstract constraint specification language ESSENCE [Frisch et al, 2005;Frisch et al, 2007;Frisch et al, 2008]. ESSENCE allows problems to be described without commitment to lowlevel modelling decisions through its support for a rich set of abstract type constructors, such as sets, multisets, sequences and relations, each of which can be nested arbitrarily.…”

Section: Introductionmentioning

confidence: 99%

Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence

Attieh

Dang

Jefferson

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

This paper presents Athanor, a novel local search solver that operates on abstract constraint specifications of combinatorial problems in the Essence language. It is unique in that it operates directly on the high level, nested types in Essence, such as set of partitions or multiset of sequences, without refining such types into low level representations. This approach has two main advantages. First, the structure present in the high level types allows high quality neighbourhoods for local search to be automatically derived. Second, it allows Athanor to scale much better than solvers that operate on the equivalent, but much larger, low-level representations. The paper details how Athanor operates, covering incremental evaluation, dynamic unrolling of quantified expressions and neighbourhood construction. A series of case studies show the performance of Athanor, benchmarked against several local search solvers on a range of problem classes.

show abstract

A Framework for Constraint Based Local Search using Essence

Cited by 6 publications

References 18 publications

Generating Local Search Neighborhood with Synthesized Logic Programs

Generating Local Search Neighborhood with Synthesized Logic Programs

Research Report on Automatic Synthesis of Local Search Neighborhood Operators

Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence

Contact Info

Product

Resources

About