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The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
 IEEE Transactions on Knowledge and Data Engineering
, 1998
"... In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various applica ..."
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Cited by 270 (22 self)
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In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Artificial Intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weakcommitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weakcommitment search algorithm ...
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 203 (7 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.
A Retrospective View of FA/C Distributed Problem Solving
, 1991
"... The FunctionallyAccurate, Cooperative (FA/C) paradigm provides a model for task decomposition and agent interaction in a distributed problemsolving system. In this model, agents need not have all the necessary information locally to solve their subproblems, and agents interact through the asynchro ..."
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Cited by 96 (25 self)
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The FunctionallyAccurate, Cooperative (FA/C) paradigm provides a model for task decomposition and agent interaction in a distributed problemsolving system. In this model, agents need not have all the necessary information locally to solve their subproblems, and agents interact through the asynchronous, coroutine exchange of partial results. This model leads to the possibility that agents may behave in an uncoordinated manner. This paper traces the development of a series of increasingly sophisticated cooperative control mechanisms for coordinating agents. They include integrating data and goaldirected control, using static metalevel information specified by an organizational structure, and using dynamic metalevel information developed in partial global planning. The framework of distributed search motivates these developments. Major themes of this work are the importance of sophisticated local control, the interplay between local control and cooperative control, and the use of s...
Environment Centered Analysis and Design of Coordination Mechanisms
, 1995
"... Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the ..."
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Cited by 88 (18 self)
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Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the activities of distributed intelligent agents appear in many domains: the control of distributed sensor networks; multiagent scheduling of people and/or machines; distributed diagnosis of errors in localarea or telephone networks; concurrent engineering; `software agents' for information gathering. The design of coordination mechanisms for group...
Generalizing the Partial Global Planning Algorithm
 INTERNATIONAL JOURNAL OF INTELLIGENT AND COOPERATIVE INFORMATION SYSTEMS
, 1992
"... The distributed coordination problem can be described as how should the local scheduling of activities at each agent be affected by nonlocal concerns and constraints. Partial global planning (PGP) is a flexible approach to distributed coordination that allows agents to respond dynamically to thei ..."
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Cited by 87 (27 self)
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The distributed coordination problem can be described as how should the local scheduling of activities at each agent be affected by nonlocal concerns and constraints. Partial global planning (PGP) is a flexible approach to distributed coordination that allows agents to respond dynamically to their current situation. It is based on detecting relationships in the computational goal structures of the distributed agents. However, the detailed PGP mechanisms depend on the existence and availability of certain characteristics and structures ...
Distributed Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems
, 1996
"... This paper presents a new algorithm for solving distributed constraint satisfaction problems (distributed CSPs) called the distributedbreakout algorithm, which is inspired by the breakout algorithm for solving centralized CSPs. In this algorithm, each agent tries to optimize its evaluation valu ..."
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Cited by 87 (14 self)
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This paper presents a new algorithm for solving distributed constraint satisfaction problems (distributed CSPs) called the distributedbreakout algorithm, which is inspired by the breakout algorithm for solving centralized CSPs. In this algorithm, each agent tries to optimize its evaluation value (the number of constraint violations) by exchanging its current value and the possible amount of its improvement among neighboring agents. Instead of detecting the fact that agents as a whole are trapped in a localminimum, each agent detects whether it is in a quasilocalminimum, which is a weaker condition than a localminimum, and changes the weights of constraint violations to escape from the quasilocalminimum. Experimental evaluations show this algorithm to be much more efficient than existing algorithms for critically difficult problem instances of distributed graphcoloring problems.
Distributed problem solving and planning
, 1999
"... Abstract. Distributed problem solving involves the collective effort of multiple problems solvers to combine their knowledge, information, and capabilities so as to develop solutions to problems that each could not have solved as well (if at all) alone. The challenge in distributed problem solving i ..."
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Cited by 74 (0 self)
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Abstract. Distributed problem solving involves the collective effort of multiple problems solvers to combine their knowledge, information, and capabilities so as to develop solutions to problems that each could not have solved as well (if at all) alone. The challenge in distributed problem solving is thus in marshalling the distributed capabilities in the right ways so that the problem solving activities of each agent complement the activities of the others, so as to lead efficiently to effective solutions. Thus, while working together leads to distributed problem solving, there is also the distributed problem of how to work together that must be solved. We consider that problem to be a distributed planning problem, where each agent must formulate plans for what it will do that take into account (sufficiently well) the plans of other agents. In this paper, we characterize the variations of distributed problem solving and distributed planning, and summarize some of the basic techniques that have been developed to date. 1
Distributed Constraint Satisfaction Algorithm for Complex Local Problems
, 1998
"... A distributed constraint satisfaction problem can formalize various application problems in MAS, and several algorithms for solving this problem have been developed. One limitation of these algorithms is that they assume each agent has only one local variable. Although simple modifications enable th ..."
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Cited by 70 (9 self)
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A distributed constraint satisfaction problem can formalize various application problems in MAS, and several algorithms for solving this problem have been developed. One limitation of these algorithms is that they assume each agent has only one local variable. Although simple modifications enable these algorithms to handle multiple local variables, obtained algorithms are neither efficient nor scalable to larger problems. We develop a new algorithm that can handle multiple local variables efficiently, which is based on the asynchronous weakcommitment search algorithm. In this algorithm, a bad local solution can be modified without forcing other agents to exhaustively search local problems. Also, the number of interactions among agents can be decreased since agents communicate only when they find local solutions that satisfy all of the local constraints. Experimental evaluations show that this algorithm is far more efficient than an algorithm that uses the prioritization among agents. 1
Reflections on the Nature of MultiAgent Coordination and Its Implications for an Agent Architecture
 Autonomous Agents and MultiAgent Systems
, 1998
"... The development of enabling infrastructure for the next generation of multiagent systems consisting of large numbers of agents and operating in open environments is one of the key challenges for the multiagent community. Current infrastructure support does not materially assist in the development ..."
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Cited by 63 (11 self)
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The development of enabling infrastructure for the next generation of multiagent systems consisting of large numbers of agents and operating in open environments is one of the key challenges for the multiagent community. Current infrastructure support does not materially assist in the development of sophisticated agent coordination strategies. It is the need for and the development of such a highlevel support structure that will be the focus of this paper. A domainindependent (generic) agent architecture is proposed that wraps around an agent’s problemsolving component in order to make problemsolving responsive to realtime constraints, available network resources and the need to coordinate — both in the large and small, with problemsolving activities of other agents. This architecture contains five components, local agent scheduling, multiagent coordination, organizational design, detection and diagnosis and online learning, that are designed to interact so that a range of different situationspecific coordination strategies can be implemented and adapted as the situation evolves. The presentation of this architecture is followed by a more detailed discussion on the interaction among these components and the
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