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Algorithms for Distributed Constraint Satisfaction: A Review
 In CP
, 2000
"... . When multiple agents are in a shared environment, there usually exist constraints among the possible actions of these agents. A distributed constraint satisfaction problem (distributed CSP) is a problem to find a consistent combination of actions that satisfies these interagent constraints. Vario ..."
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Cited by 208 (8 self)
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. When multiple agents are in a shared environment, there usually exist constraints among the possible actions of these agents. A distributed constraint satisfaction problem (distributed CSP) is a problem to find a consistent combination of actions that satisfies these interagent constraints. Various application problems in multiagent systems can be formalized as distributed CSPs. This paper gives an overview of the existing research on distributed CSPs. First, we briefly describe the problem formalization and algorithms of normal, centralized CSPs. Then, we show the problem formalization and several MAS application problems of distributed CSPs. Furthermore, we describe a series of algorithms for solving distributed CSPs, i.e., the asynchronous backtracking, the asynchronous weakcommitment search, the distributed breakout, and distributed consistency algorithms. Finally,we showtwo extensions of the basic problem formalization of distributed CSPs, i.e., handling multiple local variables, and dealing with overconstrained problems. Keywords: Constraint Satisfaction, Search, distributed AI 1.
Nogood Recording for Static and Dynamic Constraint Satisfaction Problems
 International Journal of Artificial Intelligence Tools
, 1993
"... Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problem (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, fo ..."
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Cited by 111 (5 self)
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Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problem (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The problem we consider here is the solution maintenance problem in such a dynamic CSP (DCSP). We propose a new class of constraint recording algorithms called Nogood Recording that may be used for solving both static and dynamic CSPs. It offers an interesting compromise, polynomially bounded in space, between an ATMSlike approach and the usual static constraint satisfaction algorithms. 1 Introduction The constraint satisfaction problem (CSP) model is widely used to represent and solve various AI related problems and provides fundamental tools in areas such as truth...
Uncertainty and change
 Handbook of Constraint Programming, chapter 21
, 2006
"... Constraint Programming (CP) has proven to be a very successful technique for reasoning about assignment problems, as evidenced by the many applications described elsewhere in this book. Much of its success is due to the simple and elegant underlying formulation: describe the world in terms of decisi ..."
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Cited by 9 (2 self)
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Constraint Programming (CP) has proven to be a very successful technique for reasoning about assignment problems, as evidenced by the many applications described elsewhere in this book. Much of its success is due to the simple and elegant underlying formulation: describe the world in terms of decision variables that must be assigned values, place clear and explicit restrictions on the values that may be assigned simultaneously, and then find a set of assignments to all the variables that obeys those restrictions. Thus, CP makes two assumptions about the problems it tackles: 1. There is no uncertainty in the problem definition: each problem has a crisp and complete description. 2. Problems are not dynamic: they do not change between the initial description and the final execution of the solution. Unfortunately, these two assumptions do not hold for many practical and important applications. For example, scheduling production in a factory is, in practice, fundamentally dynamic and uncertain: the full set of jobs to be scheduled is not known in advance, and continues to grow as existing jobs are being completed; machines break down; raw material
Solving a reallife time tabling and transportation problem using distributed CSP techniques
 in Proc. CP ’96 Workshop Constraint Programming Applications
, 1996
"... Many reallife problems in the domain of resource allocation and scheduling require Solutions which are composed of several approaches or techniques. Often, complex problems are divided into subproblems, subproblems ..."
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Cited by 7 (0 self)
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Many reallife problems in the domain of resource allocation and scheduling require Solutions which are composed of several approaches or techniques. Often, complex problems are divided into subproblems, subproblems
Two Approaches to the Solution Maintenance Problem in Dynamic Constraint Satisfaction Problems
 In Proceedings of the IJCAI93/SIGMAN Workshop on Knowledgebased Production Planning, Scheduling and Control
, 1993
"... Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many ..."
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Cited by 6 (2 self)
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Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The notion of dynamic CSP (DCSP) [DD88] has been proposed to represent such evolutions. The problem we consider here is the solution maintenance problem in a DCSP. Naively applying usual satisfaction algorithms to this problem results in redundant search and inefficiency. Two distinct approaches to this problem are proposed in this paper. The first one, called the Local Changes approach, reuses the previous solution (if any) to compute a new solution. The second one, called the Nogood Recording approach, tries to store in a concise way, an approximate description of the front...
Distributed Constraint Satisfaction Problems  A Model and Application
, 1997
"... A canonical model for distributed constraint satisfaction problems (DCSP) is presented and algorithms for processing constraints on a DCSP are described. The central idea behind a constraint network that is given as a DCSP is the existence of large differences between the cost of message passing amo ..."
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Cited by 3 (0 self)
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A canonical model for distributed constraint satisfaction problems (DCSP) is presented and algorithms for processing constraints on a DCSP are described. The central idea behind a constraint network that is given as a DCSP is the existence of large differences between the cost of message passing among different components of a DCSP and the cost of local consistency checks within each component. Therefore, algorithms which operate in a multiagent environment to solve these problems are required to take into consideration these differences. Four basic algorithms for solving DCSPs that are sound and complete are proposed. Two of these algorithms are sequential and two algorithms operate in parallel and are inherently distributed. The behavior of the proposed algorithms was tested by generating and solving a set of random DCSPs (for a variety of parameters of density and homogeneity ). The results show the superiority of the parallel algorithms when the cost variances are large. A serie...
A distributed breakout algorithm for solving a largescale project scheduling problem
 In In Proceedings of Autonomous Agents and MultiAgent Systems (AAMAS2002) Workshop on Distributed Constraint Reasoning
, 2002
"... This paper describes an application, an algorithm and the modeling of a distributed CSP. A reallife, distributed, resource constrained, largescale, projectscheduling problem is introduced. This problem is modeled and formalized as a distributed CSP. It is discussed and demonstrated how local, het ..."
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Cited by 1 (0 self)
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This paper describes an application, an algorithm and the modeling of a distributed CSP. A reallife, distributed, resource constrained, largescale, projectscheduling problem is introduced. This problem is modeled and formalized as a distributed CSP. It is discussed and demonstrated how local, heterogeneous and ambiguous project coordination information of the project partners can be exchanged unambiguously by using a project planning ontology and by translating coordination information into domain variable values and constraints. Moreover a coordination method is presented which is designed towards solving the largescale problem. This coordination method includes two separate algorithms. The first algorithm is a distributed forward pass, which computes an initial variable value assignment in polynomial time. The initial variable value assignment is then used as the entry point for the second algorithm. The second algorithm is a distributed breakout algorithm, which iteratively improves the initial variable value assignment with a conflictminimizing strategy until a solution is obtained. For improving the search efficiency of the breakout algorithm two techniques, domain filtering and single sided precedence constraints, are implemented. The overall coordination algorithm as well as the efficiency techniques are tested and evaluated with three real subproblems as well as a set of random problems. 1
A Preliminary Review of Literature on Parallel Constraint Solving
"... Abstract. With the ubiquity of multicore computing, and the likely expansion of it, it seems irresponsible for constraints researchers to ignore the implications of it. Therefore, the authors have recently begun investigating the literature in constraints on exploitation of parallel systems for cons ..."
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Abstract. With the ubiquity of multicore computing, and the likely expansion of it, it seems irresponsible for constraints researchers to ignore the implications of it. Therefore, the authors have recently begun investigating the literature in constraints on exploitation of parallel systems for constraint solving. We have been compiling an incomplete, biased, and illwritten review of this literature. While accepting these faults, we nevertheless hope that it may provide some useful pointers to others wishing to follow a similar path to us: that is a path from complete to only partial ignorance. 1
A MultiAgent System for Integrating a LargeScale Project
"... Abstract. This paper covers three areas; it describes an application, it describes the modeling, and it presents an algorithm of a distributed constraint satisfaction problem (DisCSP). The application concerns a reallife, distributed, resource constrained, largescale projectscheduling problem, wh ..."
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Abstract. This paper covers three areas; it describes an application, it describes the modeling, and it presents an algorithm of a distributed constraint satisfaction problem (DisCSP). The application concerns a reallife, distributed, resource constrained, largescale projectscheduling problem, which is introduced, modeled and formalized as DisCSP. Since the distributed schedules of the project are heterogeneous, the unambiguous exchange of information for realizing coordination is a great challenge. It is therefore discussed and demonstrated in this paper how the local and heterogeneous project coordination information can be exchanged unambiguously amongst the distributed projects by using a project planning ontology and by translating coordination information into domain variable values and constraints. Moreover, a distributed coordination method is presented for solving this largescale problem, which includes the vast amount of over 30,000 variables. The distributed coordination method includes two separate algorithms. The first algorithm is a distributed forward pass, which computes an initial variable value assignment in polynomial time. This initial variable value assignment is used as the entry point for the second algorithm, which is a distributed breakout algorithm (DisBA). The distributed breakout algorithm iteratively improves the initial variable value assignment, using a conflictminimizing strategy, until a solution is obtained. For improving the search efficiency of the breakout algorithm, domain filtering and a problem specific efficiency technique, “single sided precedence constraints”, are implemented. The overall coordination algorithm as well as the efficiency techniques are tested and evaluated with three real subproblems as well as with a set of random problems. 1
Abstract—We recently proposed NogoodBased Asynchronous
, 2012
"... Forward Checking (AFCng), an efficient and robust algorithm for solving Distributed Constraint Satisfaction Problems (DisCSPs). AFCng performs an asynchronous forward checking phase during synchronous search. In this paper, we propose two new algorithms based on the same mechanism as AFCng. Howe ..."
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Forward Checking (AFCng), an efficient and robust algorithm for solving Distributed Constraint Satisfaction Problems (DisCSPs). AFCng performs an asynchronous forward checking phase during synchronous search. In this paper, we propose two new algorithms based on the same mechanism as AFCng. However, instead of using forward checking as a filtering property, we propose to maintain arc consistency asynchronously (MACA). The first algorithm we propose, MACAdel, enforces arc consistency thanks to an additional type of messages, deletion messages. The second algorithm, MACAnot, achieves arc consistency without any new type of message. We provide a theoretical analysis and an experimental evaluation of the proposed approach. Our experiments show the good performance of MACA algorithms, particularly those of MACAnot. Index Terms—constraint reasoning, distributed constraint solving, maintaining arc consistency I.