Incentive Compatible Multiagent Constraint Optimization

Petcu, Adrian; Faltings, Boi

doi:10.1007/11600930_71

Cited by 180 publications

(302 citation statements)

References 12 publications

Supporting

Mentioning

294

Contrasting

Unclassified

Order By: Relevance

“…ADOPT [1] and DPOP [7] have been proposed as exact methods for DCOP. ADOPT performs as distributed version of branch and bound/A * search based on depth first search tree for constraint network.…”

Section: Distributed Constraint Optimization Problemmentioning

confidence: 99%

See 1 more Smart Citation

Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Ota

Matsui

Matsuo

2009

Principles of Practice in Multi-Agent Systems

View full text Add to dashboard Cite

Abstract. Distributed sensor network is an important research area of multi-agent systems. We focus on a type of distributed sensor network systems that cooperatively observe multiple targets with multiple autonomous sensors that can control their own view. The problem of allocating observation resource of the distributed sensor network can be formalized as distributed constraint optimization problems. However, in the previous works, the computation cost to solve the resource allocation problem highly increases with its scale/density. In this work, we divide the problem into two layers of problems, and two layered cooperative solvers are applied to those problems. The result of the experiment shows that our proposed method reduces the number of message cycles.

show abstract

Section: Distributed Constraint Optimization Problemmentioning

confidence: 99%

“…DPOP is based on dynamic programming. In these methods, search iterations or memory uses exponentially increase according to induced-width [7] of the depth first search tree. On the other hand, DSA [2] and DSTS [3] have been proposed as stochastic algorithms.…”

Section: Distributed Constraint Optimization Problemmentioning

confidence: 99%

Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Ota

Matsui

Matsuo

2009

Principles of Practice in Multi-Agent Systems

View full text Add to dashboard Cite

show abstract

“…A Distributed Constraint Optimization Problem (DCOP) [8,10,12] is a fundamental problem that can formalize various applications related to multi-agent cooperation. A DCOP consists of a set of agents, each of which needs to decide the value assignment of its variables so that the sum of the resulting costs is minimized.…”

Section: Introductionmentioning

confidence: 99%

“…A DCOP consists of a set of agents, each of which needs to decide the value assignment of its variables so that the sum of the resulting costs is minimized. In the last decade, various algorithms have been developed to efficiently solve DCOPs, e.g., ADOPT [10], BnB-ADOPT [15], DPOP [12], AFB [3], and ConcFB [11]. Many multi-agent coordination problems can be represented as DCOPs, e.g., distributed resource allocation problems including sensor networks [6], meeting scheduling [7], and the synchronization of traffic lights [5].…”

Section: Introductionmentioning

confidence: 99%

Model and Algorithm for Dynamic Multi-Objective Distributed Optimization

Clement

Okimoto

Ribeiro

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Many problems in multi-agent systems can be represented as a Distributed Constraint Optimization Problem (DCOP) where the goal is to find the best assignment to variables in order to minimize the cost. More complex problems including several criteria can be represented as a Multi-Objective Distributed Constraint Optimization Problem (MO-DCOP) where the goal is to optimize several criteria at the same time. However, many problems are subject to changes over time and need to be represented as dynamic problems. In this paper, we formalize the Dynamic Multi-Objective Distributed Constraint Optimization Problem (DMO-DCOP) and introduce the first algorithm called DMOBB to handle changes in the number of objectives.

show abstract

“…There has been high demand for distributed combinatorial optimization in various fields, such as resource scheduling [13], supply-chain management [19], and multi-robot coordination [4,8]. Some researchers have also tried to develop protocols for general problem formulations, such as the non-linear programming problem [1] and the constraint optimization problem [15,16,17,21,22,24,25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Price Update in Distributed Lagrangian Relaxation Protocol

Hirayama

Matsui

Yokoo

2011

Transactions of the Japanese Society for Artificial Intelligenc

View full text Add to dashboard Cite

Distributed Lagrangian Relaxation Protocol (DisLRP) has been proposed to solve a distributed combinatorial maximization problem called the Generalized Mutual Assignment Problem (GMAP). In DisLRP, when updating Lagrange multipliers (prices) of goods, the agents basically control their step length, which determines the degree of update, by a static rule. A merit of this updating rule is that since it is static, it is easy to implement even without a central control. Furthermore, if we choose this static rule appropriately, we have observed empirically that DisLRP converges to a state providing a good upper bound. However, it must be difficult to devise such a good static rule for updating step length since it naturally depends on problem instances to be solved. On the other hand, in a centralized context, the Lagrangian relaxation approach has conventionally computed step length by exploiting the least upper bound obtained during the search and a lower bound obtained through preprocessing. In this paper, we achieve this approach in a distributed environment where no central control exists and name the resultant protocol Adaptive DisLRP (ADisLRP). The key ideas of this new protocol are to 1) compute global information with a spanning tree, 2) update step length simultaneously with a synchronization protocol, and 3) estimate lower bounds during the search. We also show the robustness of ADisLRP through experiments where we compared ADisLRP with the previous protocols on the critically hard benchmark instances.

show abstract

Incentive Compatible Multiagent Constraint Optimization

Cited by 180 publications

References 12 publications

Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Model and Algorithm for Dynamic Multi-Objective Distributed Optimization

Adaptive Price Update in Distributed Lagrangian Relaxation Protocol

Contact Info

Product

Resources

About