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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 29 / No.1: The Twenty-Ninth Conference on Artificial Intelligence

Binarisation via Dualisation for Valued Constraints

March 8, 2023

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Authors

David Cohen

Royal Holloway, University of London


Martin Cooper

IRIT, University of Toulouse III


Peter Jeavons

University of Oxford


Stanislav Zivny

University of Oxford


DOI:

10.1609/aaai.v29i1.9749


Abstract:

Constraint programming is a natural paradigm for many combinatorial optimisation problems. The complexity of constraint satisfaction for various forms of constraints has been widely-studied, both to inform the choice of appropriate algorithms, and to understand better the boundary between polynomial-time complexity and NP-hardness. In constraint programming it is well-known that any constraint satisfaction problem can be converted to an equivalent binary problem using the so-called dual encoding. Using this standard approach any fixed collection of constraints, of arbitrary arity, can be converted to an equivalent set of constraints of arity at most two. Here we show that this transformation, although it changes the domain of the constraints, preserves all the relevant algebraic properties that determine the complexity. Moreover, we show that the dual encoding preserves many of the key algorithmic properties of the original instance. We also show that this remains true for more general valued constraint languages, where constraints may assign different cost values to different assignments. Hence, we obtain a simple proof of the fact that to classify the computational complexity of all valued constraint languages it suffices to classify only binary valued constraint languages.

Topics: AAAI

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HOW TO CITE:

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny Binarisation via Dualisation for Valued Constraints Proceedings of the AAAI Conference on Artificial Intelligence, 29 (2015) .

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny Binarisation via Dualisation for Valued Constraints AAAI 2015, .

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny (2015). Binarisation via Dualisation for Valued Constraints. Proceedings of the AAAI Conference on Artificial Intelligence, 29, .

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny. Binarisation via Dualisation for Valued Constraints. Proceedings of the AAAI Conference on Artificial Intelligence, 29 2015 p..

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny. 2015. Binarisation via Dualisation for Valued Constraints. "Proceedings of the AAAI Conference on Artificial Intelligence, 29". .

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny. (2015) "Binarisation via Dualisation for Valued Constraints", Proceedings of the AAAI Conference on Artificial Intelligence, 29, p.

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny, "Binarisation via Dualisation for Valued Constraints", AAAI, p., 2015.

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny. "Binarisation via Dualisation for Valued Constraints". Proceedings of the AAAI Conference on Artificial Intelligence, 29, 2015, p..

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny. "Binarisation via Dualisation for Valued Constraints". Proceedings of the AAAI Conference on Artificial Intelligence, 29, (2015): .

David Cohen|| Martin Cooper|| Peter Jeavons|| Stanislav Zivny. Binarisation via Dualisation for Valued Constraints. AAAI[Internet]. 2015[cited 2023]; .


ISSN: 2374-3468


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