TY - GEN
T1 - Propagating regular counting constraints
AU - Beldiceanu, Nicolas
AU - Flener, Pierre
AU - Pearson, Justin
AU - Van Hentenryck, Pascal
N1 - Publisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2014
Y1 - 2014
N2 - Constraints 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.
AB - Constraints 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.
UR - http://www.scopus.com/inward/record.url?scp=84908176495&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 2616
EP - 2622
BT - Proceedings of the National Conference on Artificial Intelligence
PB - AI Access Foundation
T2 - 28th 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
Y2 - 27 July 2014 through 31 July 2014
ER -