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Distributed partial constraint satisfaction problem
 Principles and Practice of Constraint Programming
, 1997
"... Abstract. Many problems in multiagent systems can be described as distributed Constraint Satisfaction Problems (distributed CSPs), where the goal is to nd a set of assignments to variables that satis es all constraints among agents. However, when real problems are formalized as distributed CSPs, th ..."
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Cited by 62 (13 self)
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Abstract. Many problems in multiagent systems can be described as distributed Constraint Satisfaction Problems (distributed CSPs), where the goal is to nd a set of assignments to variables that satis es all constraints among agents. However, when real problems are formalized as distributed CSPs, they are often overconstrained and have no solution that satis es all constraints. This paper provides the Distributed Partial Constraint Satisfaction Problem (DPCSP) as a new framework for dealing with overconstrained situations. We also present new algorithms for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs), which belong to an important class of DPCSP. The algorithms are called the Synchronous Branch and Bound (SBB) and the Iterative Distributed Breakout (IDB). Both algorithms were tested on hard classes of overconstrained random binary distributed CSPs. The results can be summarized as SBB is preferable when we are mainly concerned with the optimality ofasolution, while IDB is preferable when we want to get a nearly optimal solution quickly. 1
MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?) on Hard Problems
 In Proceedings of the Second International Conference on Principles and Practice of Constraint Programming
, 1996
"... . In the last twenty years, many algorithms and heuristics were developed to find solutions in constraint networks. Their number increased to such an extent that it quickly became necessary to compare their performances in order to propose a small number of "good" methods. These comparisons often le ..."
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Cited by 40 (3 self)
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. In the last twenty years, many algorithms and heuristics were developed to find solutions in constraint networks. Their number increased to such an extent that it quickly became necessary to compare their performances in order to propose a small number of "good" methods. These comparisons often led us to consider FC or FCCBJ associated with a "minimum domain" variable ordering heuristic as the best techniques to solve a wide variety of constraint networks. In this paper, we first try to convince once and for all the CSP community that MAC is not only more efficient than FC to solve large practical problems, but it is also really more efficient than FC on hard and large random problems. Afterwards, we introduce an original and efficient way to combine variable ordering heuristics. Finally, we conjecture that when a good variable ordering heuristic is used, CBJ becomes an expensive gadget which almost always slows down the search, even if it saves a few constraint checks. 1 Introducti...
Partial constraint satisfaction problems and guided local search
 Proc., Practical Application of Constraint Technology (PACT'96
, 1996
"... A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for sol ..."
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Cited by 31 (12 self)
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A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for solutions which violate the minimum number of constraints. In more realistic settings, constraints violations incur different costs and solutions are sought that minimize the total cost from constraint violations and possibly other criteria. Problems in this category present enormous difficulty to complete search algorithms. In practical terms, complete search has more or less to resemble the traditional Branch and Bound taking no advantage of the efficient pruning techniques recently developed for CSPs. In this report, we examine how the stochastic search method of Guided Local Search (GLS) can be applied to these problems. The effectiveness of the method is demonstrated on instances of the Radio Link Frequency Assignment Problem (RLFAP), which is a realworld Partial CSP.
Asynchronous forwardbounding for distributed constraints optimization
 In: Proc. 1st Intern. Workshop on Distributed and Speculative Constraint Processing. (2005
, 2006
"... A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one ..."
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Cited by 23 (4 self)
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A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one consistent partial assignment at all times. Forward bounding propagates the bounds on the cost of solutions by sending copies of the partial assignment to all unassigned agents concurrently. The algorithm is described in detail and its correctness proven. Experimental evaluation of AFB on random MaxDisCSPs reveals a phase transition as the tightness of the problem increases. This effect is analogous to the phase transition of MaxCSP when local consistency maintenance is applied [3]. AFB outperforms Synchronous Branch & Bound (SBB) as well as the asynchronous stateoftheart ADOPT algorithm, for the harder problem instances. Both asynchronous algorithms outperform SBB by a large factor. 1
Comparative Studies of Constraint Satisfaction and DavisPutnam Algorithms for Maximum Satisfiability Problems
 Cliques, Coloring and Satisfiability
"... Maximum satisfiability (MAXSAT) is an extension of satisfiability (SAT), in which a partial solution is sought that satisfies the maximum number of clauses in a logical formula. Enumerative methods giving guaranteed optimal solutions can be derived from traditional search algorithms used to solve S ..."
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Cited by 19 (0 self)
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Maximum satisfiability (MAXSAT) is an extension of satisfiability (SAT), in which a partial solution is sought that satisfies the maximum number of clauses in a logical formula. Enumerative methods giving guaranteed optimal solutions can be derived from traditional search algorithms used to solve SAT problems, in particular the Davis Putnam procedure. Algorithms have also been developed for the maximal constraint satisfaction problem (MAXCSP), a generalization of MAXSAT, that are extensions of search algorithms used to solve constraint satisfaction problems. In the present work, these algorithms were compared over the same sets of problems, using comparable implementations. In addition, variants of each algorithm were tested to determine the contribution of component strategies that often make up a working algorithm. The componential analysis was done using traditional multifactor experimental designs in which the effect of different strategies could be studied at the same time th...
Enhancements of Branch and Bound Methods for the Maximal Constraint Satisfaction Problem
 Proc. of AAAI96
, 1996
"... Two methods are described for enhancing performance of branch and bound methods for overconstrained CSPs. These methods improve either the upper or lower bound, respectively, during search, so the two can be combined. Upper bounds are improved by using heuristic repair methods before search to find ..."
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Cited by 17 (0 self)
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Two methods are described for enhancing performance of branch and bound methods for overconstrained CSPs. These methods improve either the upper or lower bound, respectively, during search, so the two can be combined. Upper bounds are improved by using heuristic repair methods before search to find a good solution quickly, whose cost is used as the initial upper bound. The method for improving lower bounds is an extension of directed arc consistency preprocessing, used in conjunction with forward checking. After computing directed arc consistency counts, inferred counts are computed for all values based on minimum counts for values of adjacent variables that are later in the search order. This inference process can be iterated, so that counts are cascaded from the end to the beginning of the search order, to augment the initial counts. Improvements in time and effort are demonstrated for both techniques using random problems. Introduction Constraint satisfaction problems (CSPs) invol...
Directed Arc Consistency Preprocessing as a Strategy for Maximal Constraint Satisfaction
, 1995
"... A constraint satisfaction problem (CSP) may be overconstrained and not admit a complete solution. Optimal solutions to such partial constraint satisfaction problems (PCSPs), in which a maximum number of constraints are satisfied, can be found using branch and bound variants of CSP algorithms. Earlie ..."
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Cited by 16 (5 self)
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A constraint satisfaction problem (CSP) may be overconstrained and not admit a complete solution. Optimal solutions to such partial constraint satisfaction problems (PCSPs), in which a maximum number of constraints are satisfied, can be found using branch and bound variants of CSP algorithms. Earlier work has shown how information gained through local consistency checking during preprocessing can be used to enhance search through value ordering heuristics and local lower bound calculations that involve only neighboring variables. The present work describes a family of strategies based on directed arc consistency testing during preprocessing. With this approach inconsistency counts associated with each value (the number of domains that offer no support for that value) are obtained that are nonredundant, since they are unidirectional. They can, therefore, be used to obtain global lower bounds that involve the entire set of variables. By computing directed arc consistency in each directi...
The Tunneling Algorithm for Partial CSPs and Combinatorial Optimization Problems
, 1994
"... Constraint satisfaction is the core of a large number of problems, notably scheduling. Because of their potential for containing the combinatorial explosion problem in constraint satisfaction, local search methods have received a lot of attention in the last few years. The problem with these methods ..."
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Cited by 9 (2 self)
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Constraint satisfaction is the core of a large number of problems, notably scheduling. Because of their potential for containing the combinatorial explosion problem in constraint satisfaction, local search methods have received a lot of attention in the last few years. The problem with these methods is that they can be trapped in local minima. GENET is a connectionist approach to constraint satisfaction. It escapes local minima by means of a weight adjustment scheme, which has been demonstrated to be highly effective. The tunneling algorithm described in this paper is an extension of GENET for optimization. The main idea is to introduce modifications to the function which is to be optimized by the network (this function mirrors the objective function which is specified in the problem). We demonstrate the outstanding performance of this algorithm on constraint satisfaction problems, constraint satisfaction optimization problems, partial constraint satisfaction problems, radio frequency ...
Conflict directed Backjumping for MaxCSPs
 In IJCAI2007
, 2007
"... Constraint Optimization problems are commonly solved using a Branch and Bound algorithm enhanced by consistency maintenance procedures (Wallace and Freuder 1993; ..."
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Cited by 2 (1 self)
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Constraint Optimization problems are commonly solved using a Branch and Bound algorithm enhanced by consistency maintenance procedures (Wallace and Freuder 1993;
Nurse Rostering as Constraint Satisfaction with Fuzzy Constraints and Inferred Control Strategies
, 2000
"... The commercial ORBIS · Dienstplan system solves complex nurse rostering problems which are represented as valued constraint satisfaction problems (VCSP) of 250 to 1200 constraint variables within a few minutes to a sufficient degree. Although this system is very successful on the German marke ..."
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Cited by 2 (0 self)
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The commercial ORBIS · Dienstplan system solves complex nurse rostering problems which are represented as valued constraint satisfaction problems (VCSP) of 250 to 1200 constraint variables within a few minutes to a sufficient degree. Although this system is very successful on the German market, its feasibility is not perfect. This system is extended in two ways: Fuzzy constraints are integrated in order to represent certain optimization tasks more accurately. Additionally, a generic method is proposed to infer search control knowledge from an abstraction of the original VCSP. After sketching the problem representation and search algorithms, which are used by the currently sold nurse rostering system, this paper describes both extensions which both require an extensive excursion through the theory of soft constraints.