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Titlebook: Recent Advances in Constraints; 14th Annual ERCIM In Javier Larrosa,Barry O’Sullivan Conference proceedings 2011 Springer Berlin Heidelberg

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樓主: 恰當
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發(fā)表于 2025-3-23 12:28:07 | 只看該作者
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發(fā)表于 2025-3-24 04:16:21 | 只看該作者
Solving Weighted Argumentation Frameworks with Soft Constraints,lated to arguments, e.g. a score representing a “fuzziness”, a “cost” or a probability level of each argument, can be represented by choosing different semiring algebraic structures. The novel idea is to provide a common computational and quantitative framework where the computation of the classical
16#
發(fā)表于 2025-3-24 09:43:23 | 只看該作者
Connecting BnB-ADOPT with Soft Arc Consistency: Initial Results,ind of problems. Several versions of this algorithm have been proposed, one of them is BnB-ADOPT which changes the nature of the original algorithm from best-first to depth-first search. With BnB-ADOPT, we can assure in some cases that the value of a variable will not be used in the optimal solution
17#
發(fā)表于 2025-3-24 13:17:12 | 只看該作者
Procedural Code Generation , Static Expansion in Modelling Languages for Constraint Programming,ng logical and algebraic notations instead of programming constructs. The transformation to an executable constraint program can be performed by fundamentally two compilation schemas: either by a static expansion of the model in a flat constraint satisfaction problem (e.g.?Zinc, Rules2CP, Essence) o
18#
發(fā)表于 2025-3-24 16:34:01 | 只看該作者
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發(fā)表于 2025-3-24 21:36:05 | 只看該作者
A Resource Cost Aware Cumulative,rch is primarily interested in scheduling problems under time and volume variable electricity costs, but the constraint equally applies to manpower scheduling when hourly rates differ over time and/or extra personnel incur higher hourly rates. We present a number of possible lower bounds on the cost
20#
發(fā)表于 2025-3-25 00:43:19 | 只看該作者
Integrating Strong Local Consistencies into Constraint Solvers, to event-based constraint solvers. We encapsulate a subset of constraints into a global constraint. This approach allows a solver to use different levels of consistency for different subsets of constraints in the same model. Moreover, we show how strong consistencies can be applied with different k
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