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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...
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.
Multiply sectioned bayesian networks and junction forests for large knowledge based systems
 Computational Intelligence
, 1993
"... Abstract — We extend lazy propagation for inference in singleagent Bayesian networks to multiagent lazy inference in multiply sectioned Bayesian networks (MSBNs). Two methods are proposed using distinct runtime structures. We prove that the new methods are exact and efficient when domain structure ..."
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Cited by 79 (28 self)
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Abstract — We extend lazy propagation for inference in singleagent Bayesian networks to multiagent lazy inference in multiply sectioned Bayesian networks (MSBNs). Two methods are proposed using distinct runtime structures. We prove that the new methods are exact and efficient when domain structure is sparse. Both improve space and time complexity than the existing method, which allow multiagent probabilistic reasoning to be performed in much larger domains given the computational resource. Relative performance of the three methods are compared analytically and experimentally. I.
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
Multiagent Systems and Societies of Agents
, 1999
"... Introduction Agents operate and exist in some environment, which typically is both computational and physical. The environment might be open or closed, and it might or might not contain other agents. Although there are situations where an agent can operate usefully by itself, the increasing intercon ..."
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Cited by 69 (0 self)
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Introduction Agents operate and exist in some environment, which typically is both computational and physical. The environment might be open or closed, and it might or might not contain other agents. Although there are situations where an agent can operate usefully by itself, the increasing interconnection and networking of computers is making such situations rare, and in the usual state of affairs the agent interacts with other agents. Whereas the previous chapter defined the structure and characteristics of an individual agent, the focus of this chapter is on systems with multiple agents. At times, the number of agents may be too numerous to deal with them individually, and it is then more convenient to deal with them collectively, as a society of agents. In this chapter, we will learn how to analyze, describe, and design environments in which agents can operate effectively and interact with each other productively. The environments will provide a computational infrastructu
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
Exploiting Problem Structure for Distributed Constraint Optimization
 In Proceedings of the First International Conference on MultiAgent Systems
, 1995
"... Distributed constraint optimization imposes considerable complexity in agents' coordinated search for an optimal solution. However, in many application domains, problems often exhibit special structures that can be exploited to facilitate more efficient problem solving. One of the most recurrent str ..."
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Cited by 39 (2 self)
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Distributed constraint optimization imposes considerable complexity in agents' coordinated search for an optimal solution. However, in many application domains, problems often exhibit special structures that can be exploited to facilitate more efficient problem solving. One of the most recurrent structures involves disparity among subproblems. We present a coordination mechanism, Anchor&Ascend, for distributed constraint optimization that takes advantage of disparity among subproblems to efficiently guide distributed local search for global optimality. The coordination mechanism assigns different overlapping subproblems to agents who must interact and iteratively converge on a solution. In particular, an anchor agent who conducts local best first search to optimize its subsolution interacts with the rest of the agents who perform distributed constraint satisfaction to enforce problem constraints and constraints imposed by the anchor agent. We focus our study on the wellknown NPcomple...
The DRESUN Testbed for Research in FA/C Distributed Situation Assessment: Extensions to the Model of External Evidence
, 1995
"... This paper reports on extensions that have been made to the DRESUN testbed for research on distributed situation assessment (DSA). These extensions involve issues that have arisen in modeling the beliefs of other agents when dealing with interagent communication of incomplete and conflicting eviden ..."
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Cited by 33 (20 self)
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This paper reports on extensions that have been made to the DRESUN testbed for research on distributed situation assessment (DSA). These extensions involve issues that have arisen in modeling the beliefs of other agents when dealing with interagent communication of incomplete and conflicting evidence, and evidence at multiple levels of abstraction. The extensions support highly directed exchanges of evidence among agents because they better represent the uncertainties that occur when DRESUN agents exchange incomplete and conflicting information. This is important in FA/C systems because agents must share results in order to satisfy their local goals as well as the overall system goals. Thus, sharing must be done efficiently for an FA/C approach to be effective. These issues will arise in any distributed problem solving application involving interacting subproblems, when agents must function without complete and uptodate information. Introduction The functionally accurate, cooperat...