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Adopt: asynchronous distributed constraint optimization with quality guarantees
 ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
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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 223 (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.
Solving distributed constraint optimization problems using cooperative mediation
 In Proceedings of AAMAS
, 2004
"... Distributed Constraint Optimization Problems (DCOP) have, for a long time, been considered an important research area for multiagent systems because a vast number of realworld situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve th ..."
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Cited by 177 (4 self)
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Distributed Constraint Optimization Problems (DCOP) have, for a long time, been considered an important research area for multiagent systems because a vast number of realworld situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve them efficiently using fully distributed algorithms which are often based on existing centralized techniques. In this paper, we present an optimal, distributed algorithm called optimal asynchronous partial overlay (OptAPO) for solving DCOPs that is based on a partial centralization technique called cooperative mediation. The key ideas used by this algorithm are that agents, when acting as a mediator, centralize relevant portions of the DCOP, that these centralized subproblems overlap, and that agents increase the size of their subproblems as the problem solving unfolds. We present empirical evidence that shows that OptAPO performs better than other known, optimal DCOP techniques. 1.
An Asynchronous Complete Method for Distributed Constraint Optimization
 In AAMAS
, 2003
"... We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to pr ..."
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Cited by 123 (31 self)
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We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both efficiently and asynchronously. Adopt is guaranteed to find an optimal solution, or a solution within a userspecified distance from the optimal, while allowing agents to execute asynchronously and in parallel. Adopt obtains these properties via a distributed search algorithm with several novel characteristics including the ability for each agent to make local decisions based on currently available information and without necessarily having global certainty. Theoretical analysis shows that Adopt provides provable quality guarantees, while experimental results show that Adopt is significanfly more efficient than synchronous methods. The speedups are shown to be partly due to the novel search strategy employed and partly due to the asynchrony of the algorithm.
BnBADOPT: An asynchronous branchandbound DCOP algorithm
 In Proceedings of AAMAS
, 2008
"... Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus introduce BranchandBound ADOPT (BnBADOPT), a memoryboun ..."
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Cited by 39 (13 self)
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Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asynchronous DCOP algorithm that uses the message passing and communication framework of ADOPT, a well known memorybounded asynchronous DCOP algorithm, but changes the search strategy of ADOPT from bestfirst search to depthfirst branchandbound search. Our experimental results show that BnBADOPT is up to one order of magnitude faster than ADOPT on a variety of large DCOPs and faster than NCBB, a memorybounded synchronous DCOP algorithm, on most of these DCOPs. 1
Cooperative negotiation for soft realtime distributed resource allocation
 in Proceedings of AAMAS’03
, 2003
"... In this paper we present a cooperative negotiation protocol that solves a distributed resource allocation problem while conforming to soft realtime constraints in a dynamic environment. Two central principles are used in this protocol that allow it to operate in constantly changing conditions. Firs ..."
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Cited by 31 (5 self)
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In this paper we present a cooperative negotiation protocol that solves a distributed resource allocation problem while conforming to soft realtime constraints in a dynamic environment. Two central principles are used in this protocol that allow it to operate in constantly changing conditions. First, we frame the allocation problem as an optimization problem, similar to a Partial Constraint Satisfaction Problem (PCSP), and use relaxation techniques to derive conflict (constraint violation) free solutions. Second, by using overlapping mediated negotiations to conduct the search, we are able to prune large parts of the search space by using a form of arcconsistency. This allows the protocol to both quickly identify situations where the problem is overconstrained and to identify the appropriate fix to the overconstrained problem. From the global perspective, the protocol has a hill climbing behavior and because it was designed to work in dynamic environments, is an approximate one. We describe the domain which inspired the creation of this protocol, as well as discuss experimental results.
An Approach to Overconstrained Distributed Constraint Satisfaction Problems: Distributed Hierarchical Constraint Satisfaction
 PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON MULTIAGENT SYSTEMS (ICMAS2000), PP.135–142
, 2000
"... Many problems in multiagent systems can be described as distributed CSPs. However, some reallife problem can be overconstrained and without a set of consistent variable values when described as a distributed CSP. We have presented the distributed partial CSP for handling such an overconstrained ..."
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Cited by 25 (5 self)
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Many problems in multiagent systems can be described as distributed CSPs. However, some reallife problem can be overconstrained and without a set of consistent variable values when described as a distributed CSP. We have presented the distributed partial CSP for handling such an overconstrained situation and the distributed maximal CSP as a subclass of distributed partial CSP. In this paper, we first show another subclass of distributed partial CSP, the distributed hierarchical CSP. Next, we present a series of new algorithms for solving a distributed hierarchical CSP, each of which is designed based on our previous distributed constraint satisfaction algorithms. Finally, we evaluate the performance of our new algorithms on distributed 3coloring problems in terms of optimality and anytime characteristics. The results show that our new algorithms perform much better than the previous algorithm for finding an optimal solution and produce good results for anytime characteristics.
Privacy loss in distributed constraint reasoning: A quantitative framework for analysis and its applications
, 2006
"... It is critical that agents deployed in realworld settings, such as businesses, offices, universities and research laboratories, protect their individual users ’ privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent ..."
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Cited by 25 (2 self)
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It is critical that agents deployed in realworld settings, such as businesses, offices, universities and research laboratories, protect their individual users ’ privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a statespace model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings
An analysis of privacy loss in distributed constraint optimization
 In AAAI
, 2006
"... Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. However, despite agent privacy being a key motivation for applying DCOPs in many applications, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking. ..."
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Cited by 17 (7 self)
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Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. However, despite agent privacy being a key motivation for applying DCOPs in many applications, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking. Recently, [Maheswaran et al.2005] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that some DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. This paper addresses the question of whether stateofthe art DCOP algorithms suffer from a similar shortcoming by investigating several of the most efficient DCOP algorithms, including both DPOP and ADOPT. Furthermore, while previous work investigated the impact on efficiency of distributed contraint reasoning design decisions (e.g. constraintgraph topology, asynchrony, messagecontents), this paper examines the privacy aspect of such decisions, providing an improved understanding of privacyefficiency tradeoffs.
Experimental analysis of privacy loss in dcop algorithms
 in AAMAS
, 2006
"... Abstract.Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCO ..."
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Cited by 16 (5 self)
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Abstract.Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [1, 2] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do stateofthe art DCOP algorithms suffer from a similar shortcoming? This paper answers that question by investigating several of the most efficient DCOP algorithms, including both DPOP and ADOPT. Furthermore, while previous work investigated the impact on efficiency of distributed contraint reasoning design decisions, e.g. constraintgraph topology, asynchrony, messagecontents, this paper examines the privacy aspect of such decisions, providing an improved understanding of privacyefficiency tradeoffs. Finally, this paper augments previous work on systemwide privacy loss, by investigating inequities in individual agents ’ privacy loss. 1