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Propagating regular counting constraints

dc.contributor.authorBeldiceanu, Nicolas
dc.contributor.authorFlener, P
dc.contributor.authorPearson, J
dc.contributor.authorVan Hentenryck, Pascal
dc.coverage.spatialQuebec Canada
dc.date.accessioned2015-12-08T22:33:14Z
dc.date.createdJuly 27-31 2014
dc.date.issued2014
dc.date.updated2015-12-08T09:34:03Z
dc.description.abstractConstraints over finite sequences of variables are ubiquitous in sequencing and timetabling. This led to general modelling techniques and generic propagators, often based on deterministic finite automata (DFA) and their extensions. We consider counter-DFAs (cDFA). which provide concise models for regular counting constraints, that is constraints over the number of times a regular-language pattern occurs in a sequence. We show how to enforce domain consistency in polynomial time for at-most and at-least regular counting constraints based on the frequent case of a cDFA with only accepting states and a single counter that can be increased by transitions. We also show that the satisfaction of exact regular counting constraints is NP-hard and that an incomplete propagator for ex-act regular counting constraints is faster and provides more pruning than the existing propagator from (Beldiceanu, Carls- son, and Petit 2004). Finally, by avoiding the unrolling of the cDFA used by CostRegular, the space complexity reduces from 0(n · |Σ| · |Q|) to 0(n · (|Σ| + |Q|))% where Σ is the alphabet and Q the state set of the cDFA.
dc.identifier.isbn9781577356776
dc.identifier.urihttp://hdl.handle.net/1885/34592
dc.publisherAAAI Press
dc.relation.ispartofseries28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
dc.sourceSequential Decision-Making with Big Data: Papers from the AAAI-14 Workshop
dc.titlePropagating regular counting constraints
dc.typeConference paper
local.bibliographicCitation.lastpage2622
local.bibliographicCitation.startpage2616
local.contributor.affiliationBeldiceanu, Nicolas, Mines de Nantes
local.contributor.affiliationFlener, P, Uppsala University
local.contributor.affiliationPearson, J, Uppsala University
local.contributor.affiliationVan Hentenryck, Pascal, College of Engineering and Computer Science, ANU
local.contributor.authoruidVan Hentenryck, Pascal, u5136864
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080300 - COMPUTER SOFTWARE
local.identifier.absfor080200 - COMPUTATION THEORY AND MATHEMATICS
local.identifier.absfor080100 - ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
local.identifier.ariespublicationa383154xPUB115
local.identifier.scopusID2-s2.0-84908176495
local.type.statusPublished Version

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