AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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A Soft Global Precedence Constraint
David Lesaint, Deepak Mehta, Barry O'Sullivan, Luis Quesada, Nic Wilson

Last modified: 2009-06-25

Abstract


Hard and soft precedence constraints  play  a key role in many application domains.  In telecommunications, one application is the configuration of call control feature subscriptions  where the task is to sequence a set of user-selected features subject to a set of hard (catalogue) precedence constraints and a set of soft (user-selected) precedence constraints. When no such consistent sequence exists, the task is to find an optimal relaxation by discarding some  features or user precedences. For this purpose, we present the global constraint SOFTPREC. Enforcing Generalized Arc Consistency  (GAC)  on SOFTPREC is NP-complete. Therefore, we approximate  GAC based on domain pruning  rules that follow from the semantics of  SOFTPREC; this pruning is polynomial. Empirical results demonstrate  that  the search effort  required by SOFTPREC is  up to one order of magnitude less than  the previously known best CP approach for the feature subscription problem. SOFTPREC is also applicable to other  problem domains  including minimum  cutset problems  for which initial experiments confirm the interest.

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