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102
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.
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
Distributed partial constraint satisfaction problem
 Principles and Practice of Constraint Programming
, 1997
"... Abstract. Many problems in multiagent systems can be described as distributed Constraint Satisfaction Problems (distributed CSPs), where the goal is to nd a set of assignments to variables that satis es all constraints among agents. However, when real problems are formalized as distributed CSPs, th ..."
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Cited by 71 (14 self)
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Abstract. Many problems in multiagent systems can be described as distributed Constraint Satisfaction Problems (distributed CSPs), where the goal is to nd a set of assignments to variables that satis es all constraints among agents. However, when real problems are formalized as distributed CSPs, they are often overconstrained and have no solution that satis es all constraints. This paper provides the Distributed Partial Constraint Satisfaction Problem (DPCSP) as a new framework for dealing with overconstrained situations. We also present new algorithms for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs), which belong to an important class of DPCSP. The algorithms are called the Synchronous Branch and Bound (SBB) and the Iterative Distributed Breakout (IDB). Both algorithms were tested on hard classes of overconstrained random binary distributed CSPs. The results can be summarized as SBB is preferable when we are mainly concerned with the optimality ofasolution, while IDB is preferable when we want to get a nearly optimal solution quickly. 1
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 64 (21 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
Quality Guarantees on kOptimal Solutions for Distributed Constraint Optimization Problems
, 2007
"... A distributed constraint optimization problem (DCOP) is a formalism that captures the rewards and costs of local interactions within a team of agents. Because complete algorithms to solve DCOPs are unsuitable for some dynamic or anytime domains, researchers have explored incomplete DCOP algorithms t ..."
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Cited by 44 (11 self)
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A distributed constraint optimization problem (DCOP) is a formalism that captures the rewards and costs of local interactions within a team of agents. Because complete algorithms to solve DCOPs are unsuitable for some dynamic or anytime domains, researchers have explored incomplete DCOP algorithms that result in locally optimal solutions. One type of categorization of such algorithms, and the solutions they produce, is koptimality; a koptimal solution is one that cannot be improved by any deviation by k or fewer agents. This paper presents the first known guarantees on solution quality for koptimal solutions. The guarantees are independent of the costs and rewards in the DCOP, and once computed can be used for any DCOP of a given constraint graph structure.
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 43 (6 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.
Distributed algorithms for DCOP: A graphicalgamebased approach
 In PDCS
, 2004
"... This paper addresses the application of distributed constraint optimization problems (DCOPs) to largescale dynamic environments. We introduce a decomposition of DCOP into a graphical game and investigate the evolution of various stochastic and deterministic algorithms. We also develop techniques th ..."
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Cited by 39 (13 self)
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This paper addresses the application of distributed constraint optimization problems (DCOPs) to largescale dynamic environments. We introduce a decomposition of DCOP into a graphical game and investigate the evolution of various stochastic and deterministic algorithms. We also develop techniques that allow for coordinated negotiation while maintaining distributed control of variables. We prove monotonicity properties of certain approaches and detail arguments about equilibrium sets that offer insight into the tradeoffs involved in leveraging efficiency and solution quality. The algorithms and ideas were tested and illustrated on several graph coloring domains. 1.
Open constraint programming
 Artifitial Intelligence
"... Constraint satisfaction and optimization problems often involve multiple participants. For example, producing an automobile involves a supply chain of many companies. Scheduling production, delivery and assembly of the different parts would best be solved as a constraint optimization problem ([35]). ..."
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Cited by 38 (5 self)
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Constraint satisfaction and optimization problems often involve multiple participants. For example, producing an automobile involves a supply chain of many companies. Scheduling production, delivery and assembly of the different parts would best be solved as a constraint optimization problem ([35]). A more familiar task for most of us is meeting scheduling: arrange a set of meetings with varying participants such that no two meetings involving the same person are scheduled at the same time, while respecting order and deadline constraints ([18, 22]). Another application that has been studied in detail is coordinating a network of distributed sensors ([2]). Such problems can of course be solved by gathering all constraints and optimization criteria into a single large CSP, and then solving this problem using a centralized algorithm. In practice there are many cases where this is not feasible, because it is impossible to bound the problem to a manageable set of variables. For example, in meeting scheduling, once two people are planning a common meeting, this meeting is potentially in conflict with many other meetings either of them are planning and whose times are decided in parallel. A centralized solver does not know beforehand
Asynchronous partial overlay: A new algorithm for solving distributed constraint satisfaction problems
 Journal of Artificial Intelligence Research (JAIR
, 2006
"... Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multiagent systems research. This is because many realworld problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we pres ..."
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Cited by 33 (3 self)
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Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multiagent systems research. This is because many realworld problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called asynchronous partial overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques. 1.
DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks
"... Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to realworld domains. Three fundamental challenges must be addressed for a class of realworld domains, requiring novel DCOP algorithms. First, a ..."
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Cited by 28 (6 self)
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Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to realworld domains. Three fundamental challenges must be addressed for a class of realworld domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decisiontheoretic exploration approaches with DCOPmandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.