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278
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.
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 135 (17 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.
Taking DCOP to the real world: efficient complete solutions for distributed multievent scheduling
 in AAMAS
, 2004
"... Distributed Constraint Optimization (DCOP) is an elegant formalism relevant to many areas in multiagent systems, yet complete algorithms have not been pursued for real world applications due to perceived complexity. To capably capture a rich class of complex problem domains, we introduce the Distrib ..."
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Cited by 106 (29 self)
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Distributed Constraint Optimization (DCOP) is an elegant formalism relevant to many areas in multiagent systems, yet complete algorithms have not been pursued for real world applications due to perceived complexity. To capably capture a rich class of complex problem domains, we introduce the Distributed MultiEvent Scheduling (DiMES) framework and design congruent DCOP formulations with binary constraints which are proven to yield the optimal solution. To approach realworld efficiency requirements, we obtain immense speedups by improving communication structure and precomputing best case bounds. Heuristics for generating better communication structures and calculating bound in a distributed manner are provided and tested on systematically developed domains for meeting scheduling and sensor networks, exemplifying the viability of complete algorithms. 1.
Argumentation as Distributed Constraint Satisfaction: Applications And Results
, 2001
"... Conflict resolution is a critical problem in distributed and collaborative multiagent systems. Negotiation via argumentation (NVA), where agents provide explicit arguments or justifications for their proposals for resolving conflicts, is an effective approach to resolve conflicts. Indeed, we are ap ..."
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Cited by 96 (16 self)
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Conflict resolution is a critical problem in distributed and collaborative multiagent systems. Negotiation via argumentation (NVA), where agents provide explicit arguments or justifications for their proposals for resolving conflicts, is an effective approach to resolve conflicts. Indeed, we are applying argumentation in some realworld multiagent applications. However, a key problem in such applications is that a wellunderstood computational model of argumentation is currently missing, making it difficult to investigate convergence and scalability of argumentation techniques, and to understand and characterize different collaborative NVA strategies in a principled manner. To alleviate these difficulties, we present distributed constraint satisfaction problem (DCSP) as a computational model for investigating NVA. We model argumentation as constraint propagation in DCSP. This model enables us to study convergence properties of argumentation, and formulate and experimentally compare 16 different NVA strategies with different levels of agent cooperativeness towards others. One surprising result from our experiments is that maximizing cooperativeness is not necessarily the best strategy even in a completely cooperative environment. The paper illustrates the usefulness of these results in applying NVA to multiagent systems, as well as to DCSP systems in general.
Using Similarity Criteria to Make Issue TradeOffs in Automated Negotiations
 Artificial Intelligence
, 2002
"... Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here ..."
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Cited by 91 (8 self)
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Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here we present a strategy called the tradeoff strategy where multiple negotiation decision variables are tradedoff against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer in order to obtain a higher quality service). Such a strategy is commonly known to increase the social welfare of agents. Yet, to date, most computational work in this area has ignored the issue of tradeoffs, instead aiming to increase social welfare through mechanism design. The aim of this paper is to develop a heuristic computational model of the tradeoff strategy and show that it can lead to an increased social welfare of the system. A novel linear algorithm is presented that enables software agents to make tradeoffs for multidimensional goods for the problem of distributed resource allocation.
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 76 (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
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 74 (21 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 Dynamic Backtracking
 In International Joint Conference on AI Workshop on Distributed Constraint Reasoning
, 2001
"... In the scope of distributed constraint reasoning, the main algorithms presented so far have a feature in common: the addition of links between previously unrelated agents, before or during search. This paper presents a new search procedure for finding a solution in a distributed constraint satisfact ..."
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Cited by 62 (2 self)
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In the scope of distributed constraint reasoning, the main algorithms presented so far have a feature in common: the addition of links between previously unrelated agents, before or during search. This paper presents a new search procedure for finding a solution in a distributed constraint satisfaction problem. This algorithm makes use of some of the good properties of centralised dynamic backtracking. It ensures the completeness of search, and allows a high level of asynchronism by sidestepping the unnecessary addition of links. 1.
Impact of problem centralization in distributed constraint optimization algorithms
 In AAMAS
, 2005
"... Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the d ..."
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Cited by 41 (4 self)
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Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the dominant evaluation metric of “number of cycles ” fails to account for this cost. We analyze the relative performance of two recent algorithms for DCOP: OptAPO, which performs partial centralization, and Adopt, which maintains distribution of the DCOP. Previous comparison of Adopt and OptAPO has found that OptAPO requires fewer cycles than Adopt. We extend the cycles metric to define “CycleBased Runtime (CBR) ” to account for both the amount of computation required in each cycle and the communication latency between cycles. Using the CBR metric, we show that Adopt outperforms OptAPO under a range of communication latencies. We also ask: What level of centralization is most suitable for a given communication latency? We use CBR to create performance curves for three algorithms that vary in degree of centralization, namely Adopt, OptAPO, and centralized Branch and Bound search.