Complex multi-stage decision making problems often involve uncertainty, for example, regarding demand or processing times. Stochastic constraint programming was proposed as a way to formulate and solve such decision problems, involving arbitrary constraints over both decision and random variables. What stochastic constraint programming currently lacks is support for the use of factorized probab...

This paper presents a method of searching for the shortest route via the most designated points with the length not exceeding the preset upper bound. The proposed algorithm can obtain the quasi-optimum route efficiently and its effectiveness is verified by applying the algorithm to the actual map data. key words: routing, genetic algorithm, designated points, upper bound constraint, building bl...

Task intervals were defined in [CL94] for disjunctive scheduling so that, in a scheduling problem, one could derive much information by focusing on some key subsets of tasks. The advantage of this approach was to shorten the size of search trees for branch&bound algorithms because more propagation was performed at each node. In this paper, we refine the propagation scheme and describe in detail...

Effective methods to reduce number of message cycles of adopt algorithm which is a complete algorithm for distributed constraint optimization problems are proposed. The Adopt algorithm can perform branch and bound search asynchronously. However, it has overhead in backtracking and searches same partial solution repeatedly. To reduce overhead of backtracking, lower and upper bound of cost of par...

Existing paraphrase systems either create successive drafts from an underlying representation , or they aim to correct texts. A diierent kind of paraphrase, also reeecting a real-world task, involves imposing, on an original text, external constraints in terms of length, read-ability, etc. This sort of paraphrase requires a new framework which allows production of a new text satisfying the cons...

Distributed constraint optimization problems enable the representation of many combinatorial problems that are distributed by nature. An important motivation for such problems is to preserve the privacy of the participating agents during the solving process. The present paper introduces a novel privacy-preserving branch and bound algorithm for this purpose. The proposed algorithm, P-SyncBB, pre...

Recently, a general definition of arc consistency (AC) for soft constraint frameworks has been proposed [1]. In this paper we specialize this definition to weighted CSP and introduce two O(ed3) enforcing algorithms. Then, we refine the definition and introduce a stronger form of arc consistency (AC*) along with two O(n2d2 + ed3) algorithms. As in the CSP case, an important application of AC is ...

In this paper we show how Constraint Programming (CP) techniques can improve the efficiency and applicability of grid-based algorithms for optimising surface contact between complex solids. We use BiGGER [1] (Bimolecular complex Generation with Global Evaluation and Ranking) to illustrate the method as applied to modelling protein interactions, an important effort in current bioinformatics. BiG...

Constraint optimization underlies many problems in AI. We present a novel algorithm for finite domain constraint optimization that generalizes branch-and-bound search by reasoning about sets of assignments rather than individual assignments. Because in many practical cases, sets of assignments can be represented implicitly and compactly using symbolic techniques such as decision diagrams, the s...

Assuming that grammar is best modeled as a set of modules each representing a constraint system, like other human cognitive faculties, these constraint systems are bound to disagree and soft constraints are needed for conflict resolution among modules. This paper outlines an approach to incorporate soft constraints among grammar modules. The paper first goes over the issue of modularity and con...