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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 323 (28 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 ...
Adopt: asynchronous distributed constraint optimization with quality guarantees
 ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
"... ..."
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 248 (11 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 Scalable Method for Multiagent Constraint Optimization
"... We present in this paper a new, complete method for distributed constraint optimization, based on dynamic programming. It is a utility propagation method, inspired by the sumproduct algorithm, which is correct only for treeshaped constraint networks. In this paper, we show how to extend that algor ..."
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Cited by 179 (18 self)
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We present in this paper a new, complete method for distributed constraint optimization, based on dynamic programming. It is a utility propagation method, inspired by the sumproduct algorithm, which is correct only for treeshaped constraint networks. In this paper, we show how to extend that algorithm to arbitrary topologies using a pseudotree arrangement of the problem graph. Our algorithm requires a linear number of messages, whose maximal size depends on the induced width along the particular pseudotree chosen. We compare our algorithm with backtracking algorithms, and present experimental results. For some problem types we report orders of magnitude fewer messages, and the ability to deal with arbitrarily large problems. Our algorithm is formulated for optimization problems, but can be easily applied to satisfaction problems as well.
Agentmediated Integrative Negotiation for Retail Electronic Commerce
 Proceedings of the Workshop on Agent Mediated Electronic Trading (AMET'98
, 1998
"... Software agents help automate a variety of tasks including those involved in buying and selling products over the Internet. Although shopping agents provide convenience for consumers and yield more efficient markets, today's firstgeneration shopping agents are limited to comparing merchant off ..."
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Cited by 123 (7 self)
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Software agents help automate a variety of tasks including those involved in buying and selling products over the Internet. Although shopping agents provide convenience for consumers and yield more efficient markets, today's firstgeneration shopping agents are limited to comparing merchant offerings only on price instead of their full range of value. As such, they do a disservice to both consumers and retailers by hiding important merchant valueadded services from consumer consideration. Likewise, the increasingly popular online auctions pit sellers against buyers in distributive negotiation tugofwars over price. This paper analyzes these approaches from economic, behavioral, and software agent perspectives then proposes integrative negotiation as a more suitable approach to retail electronic commerce. Finally, we identify promising techniques (e.g., multiattribute utility theory, distributed constraint satisfaction, and conjoint analysis) for implementing agentmediated integrative negotiation. 1.
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 103 (16 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.
Backtracking in distributed constraint networks
 International Journal on Artificial Intelligence Tools
, 1998
"... The adaptation of software technology to distributed environments is an important challenge today. In this work we combine parallel and distributed search. By this way we add the potential speedup of a parallel exploration in the processing of distributed problems. This paper extends DIBT, a distri ..."
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Cited by 90 (15 self)
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The adaptation of software technology to distributed environments is an important challenge today. In this work we combine parallel and distributed search. By this way we add the potential speedup of a parallel exploration in the processing of distributed problems. This paper extends DIBT, a distributed search procedure operating in distributed constraint networks [6]. The extension is twofold. First the procedure is updated to face delayed information problems upcoming in heterogeneous systems. Second, the search is extended to simultaneously explore independent parts of a distributed search tree. By this way we introduce parallelism into distributed search, which brings to Interleaved Distributed Intelligent BackTracking (IDIBT). Our results show that 1) insoluble problems do not greatly degrade performance over DIBT and 2) superlinear speedup can be achieved when the distribution of solution is nonuniform.
Comparing Performance of Distributed Constraints Processing Algorithms
, 2002
"... Search algorithms on distributed constraints satisfaction problems, DisCSPs, are composed of agents performing computations concurrently. The most common abstract performance measurement that has been universally adopted for centralized CSPs algorithms is the number of constraints checks performed. ..."
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Cited by 86 (24 self)
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Search algorithms on distributed constraints satisfaction problems, DisCSPs, are composed of agents performing computations concurrently. The most common abstract performance measurement that has been universally adopted for centralized CSPs algorithms is the number of constraints checks performed. However, when it comes to distributed search, constraints checks are performed concurrently by all agents on the network and therefore a simple measurement of constraints checks is not adequate any more. In order to be able to compare the behavior of different algorithms, there is a need for a new distributed method to measure the search effort of a DisCSP algorithm.
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 80 (11 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