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Adopt: asynchronous distributed constraint optimization with quality guarantees
 ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
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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 132 (30 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.
Bumping strategies for the multiagent agreement problem
 In Proceedings of Autonomous Agents and MultiAgent Systems, (AAMAS
, 2005
"... We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints ..."
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Cited by 27 (1 self)
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We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. The goal is to represent problems in which agents must agree on scheduling decisions, for example, to agree on the start time of a meeting. We investigate a challenging class of MAP – private, incremental MAP (piMAP) in which agents do incremental scheduling of activities and there exist privacy restrictions on information exchange. We investigate a range of strategies for piMAP, called “bumping ” strategies. We empirically evaluate these strategies in the domain of calendar management where a personal assistant agent must schedule meetings on behalf of its human user. Our results show that bumping decisions based on scheduling difficulty models of other agents can significantly improve performance over simpler bumping strategies.
Distributed Constraint Reasoning under Unreliable Communication
 In Proceedings of Distributed Constraint Reasoning Workshop at Second International Joint Conference on Autonomous Agents and MultiAgent Systems
, 2004
"... We investigate how algorithms for Distributed Constraint Reasoning (DCR) can be modified to operate effectively over unreliable communication infrastructure. While DCR algorithms typically assume that communication is perfect, this assumption is problematic because unreliable communication is a ..."
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Cited by 10 (1 self)
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We investigate how algorithms for Distributed Constraint Reasoning (DCR) can be modified to operate effectively over unreliable communication infrastructure. While DCR algorithms typically assume that communication is perfect, this assumption is problematic because unreliable communication is a common feature of many realworld multiagent domains.
Constraints and AI planning
 IEEE Intelligent Systems
, 2005
"... Tackling realworld problems often requires to take various types of constraints into account. Such constraint types range from simple numerical comparators to complex resources. This article describes how planning techniques can be integrated with general constraintsolving frameworks, like SAT, IP ..."
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Cited by 7 (0 self)
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Tackling realworld problems often requires to take various types of constraints into account. Such constraint types range from simple numerical comparators to complex resources. This article describes how planning techniques can be integrated with general constraintsolving frameworks, like SAT, IP and CP. In many cases, the complete planning problem can be cast in these frameworks. 1
Distributed Logbased Reconciliation
 In European Conference on Artificial Intelligence Riva del Garda, Italy
, 2006
"... Computer Supported Cooperative Work (CSCW) defines software tools and technology to support groups of people working together on a project, often at different sites [5]. In this work, we present four distributed algorithms for logbased reconciliation, an important NPhard problem occurring in CSCW. ..."
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Cited by 5 (0 self)
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Computer Supported Cooperative Work (CSCW) defines software tools and technology to support groups of people working together on a project, often at different sites [5]. In this work, we present four distributed algorithms for logbased reconciliation, an important NPhard problem occurring in CSCW. Our methods remove the classical drawbacks of centralized systems like single point of failure, performance bottleneck and loss of autonomy. The problem is formalized using the Distributed Constraint Satisfaction paradigm (DisCSP). In the worst case, the message passing complexity of our methods ranges from O(p 2) to O(2 p) in a system of p nodes. Experimental results confirm our theoretical analysis and allow us to establish quality and efficiency tradeoff for each method.
Distributed Resource Allocation: Formalization, Complexity Results and Mappings to Distributed CSPs
, 2002
"... In distributed resource allocation a set of agents must assign their resources to a set of dynamic tasks. This problem arises in many realworld domains like the one described in this paper: distributed sensor networks. Despite the variety of approaches proposed for distributed resource allocatio ..."
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Cited by 4 (0 self)
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In distributed resource allocation a set of agents must assign their resources to a set of dynamic tasks. This problem arises in many realworld domains like the one described in this paper: distributed sensor networks. Despite the variety of approaches proposed for distributed resource allocation, a systematic formalization of the problem and a general solution strategy are missing. Such formalizations are necessary to understand the complexity of different types of problems and to develop solution strategies that translate across domains. This paper takes a step towards this goal by proposing a formalization of resource allocation that represents both dynamic and distributed aspects of the problem and allows tractable subclasses to be identified. In addition
Algorithmic and Domain Centralization in Distributed Constraint Optimization Problems
, 2005
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Solving Distributed Constraint Optimization Problems Optimally, Efficiently and Asynchronously
"... The Distributed Constraint Optimization Problem (DCOP) is able to model a wide variety of realworld distributed problems in multiagent systems. Unfortunately, existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both eciently and asynch ..."
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Cited by 1 (0 self)
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The Distributed Constraint Optimization Problem (DCOP) is able to model a wide variety of realworld distributed problems in multiagent systems. Unfortunately, existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both eciently and asynchronously. We present a new polynomialspace algorithm, named Adopt, for DCOP that 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 novel distributed search strategy where agents are able to make local decisions without necessarily having global certainty. Instead, local decisions are based on conservative cost estimates which become increasingly more accurate as asynchronous messages are received from neighbors in the constraint network. A detailed theoretical analysis shows that Adopt is guaranteed to terminate with the globally optimal solution. Detailed experimental results show that Adopt provides speedups of several orders of magnitude over the only existing optimal DCOP algorithm. We also provide data on the number of messages required by Adopt to nd the optimal solution. For timelimited situations, Adopt can also perform boundederror approximation  the ability to quickly find approximate solutions and, unlike heuristic or local search methods, still maintain a theoretical guarantee on global solution quality.
Composing POMDPbased Building Blocks to Analyze Largescale Multiagent Systems
, 2003
"... Given a large group of cooperative agents, selecting the right coordination or conflict resolution strategy can have a significant impact on their performance (e.g., speed of convergence) . While performance models of such coordination or conflict resolution strategies could aid in selecting the ..."
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Given a large group of cooperative agents, selecting the right coordination or conflict resolution strategy can have a significant impact on their performance (e.g., speed of convergence) . While performance models of such coordination or conflict resolution strategies could aid in selecting the right strategy for a given domain, such models remain largely uninvestigated in the multiagent literature. This paper takes a step towards applying the recently emerging distributed POMDP (partially observable markov decision process) frameworks, such as the MTDP (markov team decision process) in service of creating such performance models.