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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 326 (27 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 250 (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.
Practical Applications of Constraint Programming
 CONSTRAINTS
, 1996
"... Constraint programming is newly flowering in industry. Several companies have recently started up to exploit the technology, and the number of industrial applications is now growing very quickly. This survey will seek, by examples, ..."
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Cited by 111 (1 self)
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Constraint programming is newly flowering in industry. Several companies have recently started up to exploit the technology, and the number of industrial applications is now growing very quickly. This survey will seek, by examples,
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 (15 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.
Probe Backtrack Search for Minimal Perturbation in Dynamic Scheduling
, 1999
"... This paper describes an algorithm designed to minimally recongure schedules in response to a changing environment. External factors have caused an existing schedule to become invalid, perhaps due to the withdrawal of resources, or because of changes to the set of scheduled activities. The total shi ..."
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Cited by 87 (14 self)
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This paper describes an algorithm designed to minimally recongure schedules in response to a changing environment. External factors have caused an existing schedule to become invalid, perhaps due to the withdrawal of resources, or because of changes to the set of scheduled activities. The total shift in the start and end times of already scheduled activities should be kept to a minimum. This optimization requirement may be captured using a linear optimization function over linear constraints. However, the disjunctive nature of the resource constraints impairs traditional mathematical programming approaches. The unimodular probing algorithm interleaves constraint programming and linear programming. The linear programming solver handles only a controlled subset of the problem constraints, to guarantee that the values returned are discrete. Using probe backtracking, a complete, repairbased method for search, these values are simply integrated into constraint programming. Unimodular p...
Local Search With Constraint Propagation and ConflictBased Heuristics
, 2002
"... Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single ap ..."
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Cited by 76 (17 self)
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Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single approach. In this paper, we present a new hybrid technique. It performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflictbased techniques to efficiently guide the search. This new technique benefits from both classical approaches: aprioripruning of the search space from filteringbased search and possible repair of early mistakes from local search. We focus on a specific version of this technique: tabu decisionrepair.Experiments done on openshop scheduling problems show that our approach competes well with the best highly specialized algorithms. 2002 Elsevier Science B.V. All rights reserved.
Efficient Solution Techniques for Disjunctive Temporal Reasoning Problems
, 2002
"... Over the past few years, a new constraintbased formalism for temporal reasoning has been developed to represent and reason about Disjunctive Temporal Problems (DTPs). The class of DTPs is significantly more expressive than other problems previously studied in constraintbased temporal reasoning. In ..."
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Cited by 70 (14 self)
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Over the past few years, a new constraintbased formalism for temporal reasoning has been developed to represent and reason about Disjunctive Temporal Problems (DTPs). The class of DTPs is significantly more expressive than other problems previously studied in constraintbased temporal reasoning. In this paper we present a new algorithm for DTP solving, called Epilitis, which integrates strategies for efficient DTP solving from the previous literature, including conflictdirected backjumping, removal of subsumed variables, and semantic branching, and further adds nogood recording as a central technique. We discuss the theoretical and technical issues that arise in successfully integrating this range of strategies with one another and with nogood recording in the context of DTP solving. Using an implementation of Epilitis, we explore the effectiveness of various combinations of strategies for solving DTPs, and based on this analysis we demonstrate that Epilitis can achieve a nearly two orderofmagnitude speedup over the previously published algorithms on benchmark problems in the DTP literature.
Dynamic Prioritization of Complex Agents in Distributed Constraint Satisfaction Problems
 IN PROCEEDINGS OF 15TH IJCAI
, 1997
"... Cooperative distributed problem solving (CDPS) by looselycoupled agents can be effectively modeled as a distributed constraint satisfaction problem (DCSP) where each agent has multiple local variables. DCSP protocols typically impose (partial) orders on agents to ensure systematic exploration ..."
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Cited by 57 (1 self)
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Cooperative distributed problem solving (CDPS) by looselycoupled agents can be effectively modeled as a distributed constraint satisfaction problem (DCSP) where each agent has multiple local variables. DCSP protocols typically impose (partial) orders on agents to ensure systematic exploration of the search space, but the ordering decisions can have a dramatic effect on the overall problemsolving effort. In this paper, we examine several heuristics for ordering agents, and conclude that the best heuristics attempt to order agents based on the cumulative difficulty of finding assignments to their local variables. Less costly heuristics are sometimes also effective depending on the structure of the variables' constraints, and we describe the tradeoffs between heuristic cost and quality. Finally, we also show that a combined heuristic, with weightings determined through...
A Hybrid Search Architecture Applied to Hard Random 3SAT and LowAutocorrelation Binary Sequences
 In Proceedings of the International Conference on Principles and Practice of Constraint Programming
, 2000
"... The hybridisation of systematic and stochastic search is an active research area with potential bene ts for realworld combinatorial problems. This paper shows that randomising the backtracking component of a systematic backtracker can improve its scalability to equal that of stochastic local searc ..."
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Cited by 44 (13 self)
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The hybridisation of systematic and stochastic search is an active research area with potential bene ts for realworld combinatorial problems. This paper shows that randomising the backtracking component of a systematic backtracker can improve its scalability to equal that of stochastic local search. The hybrid may be viewed as stochastic local search in a constrained space, cleanly combining local search with constraint programming techniques. The approach is applied to two very dierent problems. Firstly a hybrid of local search and constraint propagation is applied to hard random 3SAT problems, and is the rst constructive search algorithm to solve very large instances. Secondly a hybrid of local search and branchandbound is applied to lowautocorrelation binary sequences (a notoriously dicult communications engineering problem), and is the rst stochastic search algorithm to nd optimal solutions. These results show that the approach is a promising one for both constraint satisfaction and optimisation problems.