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317
Asynchronous forwardbounding for distributed constraints optimization
 In: Proc. 1st Intern. Workshop on Distributed and Speculative Constraint Processing. (2005
, 2006
"... A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one ..."
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Cited by 47 (8 self)
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A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one consistent partial assignment at all times. Forward bounding propagates the bounds on the cost of solutions by sending copies of the partial assignment to all unassigned agents concurrently. The algorithm is described in detail and its correctness proven. Experimental evaluation of AFB on random MaxDisCSPs reveals a phase transition as the tightness of the problem increases. This effect is analogous to the phase transition of MaxCSP when local consistency maintenance is applied [3]. AFB outperforms Synchronous Branch & Bound (SBB) as well as the asynchronous stateoftheart ADOPT algorithm, for the harder problem instances. Both asynchronous algorithms outperform SBB by a large factor. 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 47 (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.
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
Constraint Solving in Uncertain and Dynamic Environments: A Survey
 Constraints
, 2005
"... Abstract. This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 [87]. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments. Keywo ..."
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Cited by 37 (4 self)
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Abstract. This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 [87]. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments. Keywords: constraint satisfaction problem, uncertainty, change, stability, robustness, flexibility
A negotiationbased Multiagent System for Supply Chain Management
 In Proceedings of Agents 99 Workshop on Agent Based DecisionSupport for Managing the InternetEnabled SupplyChain
, 1999
"... This paper describes an ongoing effort in developing a Multiagent System (MAS) for supply chain management. In our framework, functional agents can join in, stay, or leave the system. The Supply Chain Management System (SCMS) functionality is implemented through agentbased negotiation. When an orde ..."
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Cited by 35 (4 self)
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This paper describes an ongoing effort in developing a Multiagent System (MAS) for supply chain management. In our framework, functional agents can join in, stay, or leave the system. The Supply Chain Management System (SCMS) functionality is implemented through agentbased negotiation. When an order arrives, a virtual supply chain may emerge from the system through automated or semiautomated negotiation processes between functional agents. We present our framework and describe a number of negotiation performatives, which can be used to construct pairwise and third party negotiation protocols for functional agent cooperation. We also explain how to formally model the negotiation process by using Colored Petri Nets (CPN) and we provide an example of establishing a virtual chain by solving a distributed constraint satisfaction problem. Keywords Negotiation, mulitiagent system, supply chain management system, negotiation perforamtive, Color Petri Net. 1. INTRODUCTION Computer softw...
Message delay and DisCSP search algorithms
 ANN MATH ARTIF INTELL (2006 ) 46 : 415–439
, 2006
"... Distributed constraint satisfaction problems (DisCSPs) are composed of agents, each holding its own variables, that are connected by constraints to variables of other agents. Due to the distributed nature of the problem, message delay can have unexpected effects on the behavior of distributed searc ..."
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Cited by 32 (18 self)
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Distributed constraint satisfaction problems (DisCSPs) are composed of agents, each holding its own variables, that are connected by constraints to variables of other agents. Due to the distributed nature of the problem, message delay can have unexpected effects on the behavior of distributed search algorithms on DisCSPs. This has been recently shown in experimental studies of asynchronous backtracking algorithms (Bejar et al., Artif. Intell., 161:117–148, 2005; Silaghi and Faltings, Artif. Intell., 161:25–54, 2005). To evaluate the impact of message delay on the run of DisCSP search algorithms, a model for distributed performance measures is presented. The model counts the number of non concurrent constraints checks, to arrive at a solution, as a non concurrent measure of distributed computation. A simpler version measures distributed computation cost by the nonconcurrent number of steps of computation. An algorithm for computing these distributed measures of computational effort is described. The realization of the model for measuring performance of distributed search algorithms is a simulator which includes the cost of message delays. Two families of distributed search algorithms on DisCSPs are investigated. Algorithms that run a single search process, and multiple search processes algorithms. The two families of algorithms are described and associated with existing algorithms. The performance of three representative algorithms of these two families is measured on randomly generated instances of DisCSPs with delayed messages. The delay of messages is found to have a strong negative effect on single search process algorithms, whether synchronous or asynchronous. Multi
Evaluating the Performance of DCOP Algorithms in a Real World, Dynamic Problem
, 2008
"... Complete algorithms have been proposed to solve problems modelled as distributed constraint optimization (DCOP). However, there are only few attempts to address real world scenarios using this formalism, mainly because of the complexity associated with those algorithms. In the present work we compar ..."
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Cited by 31 (1 self)
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Complete algorithms have been proposed to solve problems modelled as distributed constraint optimization (DCOP). However, there are only few attempts to address real world scenarios using this formalism, mainly because of the complexity associated with those algorithms. In the present work we compare three complete algorithms for DCOP, aiming at studying how they perform in complex and dynamic scenarios of increasing sizes. In order to assess their performance we measure not only standard quantities such as number of cycles to arrive to a solution, size and quantity of exchanged messages, but also computing time and quality of the solution which is related to the particular domain we use. This study can shed light in the issues of how the algorithms perform when applied to problems other than those reported in the literature (graph coloring, meeting scheduling, and distributed sensor network).
A general, fully distributed multiagent planning algorithm
 In Proceedings of AAMAS’10
, 2010
"... We present a fully distributed multiagent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods t ..."
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Cited by 31 (6 self)
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We present a fully distributed multiagent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods to take advantage of the structure of the underlying planning problem. In multiagent planning domains with limited agent interaction, our algorithm empirically shows scalability beyond state of the art centralized solvers. Our work also provides a novel, realworld setting for testing and evaluating distributed constraint satisfaction algorithms in structured domains and illustrates how existing techniques can be altered to address such structure. Categories and Subject Descriptors