Results 1 
7 of
7
Max/minsum distributed constraint optimization through value propagation on an alternating dag
 In Proceedings of The Eleventh International Conference on Autonomous Agents and Multiagent Systems
, 2012
"... Distributed Constraint Optimization Problems (DCOPs) are NPhard and therefore the number of studies that consider incomplete algorithms for solving them is growing. Specifically, the Maxsum algorithm has drawn attention in recent years and has been applied to a number of realistic applications. Unf ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
Distributed Constraint Optimization Problems (DCOPs) are NPhard and therefore the number of studies that consider incomplete algorithms for solving them is growing. Specifically, the Maxsum algorithm has drawn attention in recent years and has been applied to a number of realistic applications. Unfortunately, in many cases Maxsum does not produce high quality solutions. More specifically, when problems include cycles of various sizes in the factor graph upon which Maxsum performs, the algorithm does not converge and the states that it visits are of low quality. In this paper we advance the research on incomplete algorithms for DCOPs by: (1) Proposing a version of the Maxsum algorithm that operates on an alternating directed acyclic graph (Maxsum_AD), which guarantees convergence in linear time. (2) Identifying major weaknesses of Maxsum and Maxsum_AD that cause inconsistent costs/utilities to be propagated and affect the assignment selection. (3) Solving the identified problems by introducing value propagation to Maxsum_AD. Our empirical study reveals a large improvement in the quality of the solutions produced by Maxsum_AD with value propagation (VP), when solving problems which include cycles, compared with the solutions produced by the standard Maxsum algorithm, Bounded Maxsum and Maxsum_AD with no value propagation.
Modeling microgrid islanding problems as dcops
 in North American Power Symposium (NAPS
, 2013
"... Abstract—In this paper, we formulate the microgrid islanding problem as distributed constraint optimization problem (DCOP) and investigate the feasibility of solving it using offtheshelf DCOP algorithms. This paper puts forward the potential of distributed constraint reasoning paradigm as a candid ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract—In this paper, we formulate the microgrid islanding problem as distributed constraint optimization problem (DCOP) and investigate the feasibility of solving it using offtheshelf DCOP algorithms. This paper puts forward the potential of distributed constraint reasoning paradigm as a candidate for solving common microgrids problems.
On MessagePassing, MAP Estimation in Graphical Models and DCOPs
"... Abstract. The maximum a posteriori (MAP) estimation problem in graphical models is a problem common in many applications such as computer vision and bioinformatics. For example, they are used to identify the most likely orientation of proteins in protein design problems. As such, researchers in the ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Abstract. The maximum a posteriori (MAP) estimation problem in graphical models is a problem common in many applications such as computer vision and bioinformatics. For example, they are used to identify the most likely orientation of proteins in protein design problems. As such, researchers in the machine learning community have developed a variety of approximate algorithms to solve them. On the other hand, distributed constraint optimization problems (DCOPs) are wellsuited for modeling many multiagent coordination problems such as the coordination of sensors in a network and the coordination of power plants. In this paper, we show that MAP estimation problems and DCOPs bear strong similarities and, as such, some approximate MAP algorithms such as iterative message passing algorithms can be easily tailored to solve DCOPs as well.
Large Neighborhood Search with Quality Guarantees for Distributed Constraint Optimization Problems Ferdinando Fioretto1,2, Federico Campeotto2,
"... Abstract. The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multiagent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NPhard. Therefore, in l ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract. The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multiagent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NPhard. Therefore, in largescale applications, incomplete DCOP algorithms are desirable. Current incomplete search techniques have subsets of the following limitations: (a) they find local minima without quality guarantees; (b) they provide loose quality assessment; or (c) they cannot exploit certain problem structures such as hard constraints. Therefore, capitalizing on strategies from the centralized constraint reasoning community, we propose to adapt the Large Neighborhood Search (LNS) strategy to solve DCOPs, resulting in the general Distributed LNS (DLNS) framework. The characteristics of this framework are as follows: (i) it is anytime; (ii) it provides quality guarantees by refining online upper and lower bounds on its solution quality; and (iii) it can learn online the best neighborhood to explore. Experimental results show that DLNS outperforms other incomplete DCOP algorithms for both random and scalefree network instances. 1
A Scalable Algorithm to Solve Distributed Constraint Optimization
"... Abstract Recently, Distributed Constraint Optimization Problems (DCOP) have been drawing a growing body of attention as an important research area in multi agent systems as a large body of real problems can be modeled by them. The primary goal of this research is to design a distributed and effecti ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract Recently, Distributed Constraint Optimization Problems (DCOP) have been drawing a growing body of attention as an important research area in multi agent systems as a large body of real problems can be modeled by them. The primary goal of this research is to design a distributed and effective algorithm to solve DCOP. There are various criteria that measure the efficiency of DCOP algorithms, but the most efficient algorithm for DCOP is the one by which the computation and communication cost is as low as possible and the quality of the solution is high. In this paper, we focus on an approximate DCOP algorithm called DALO (Distributed Asynchronous Local Optimization). Using the main idea of the DALO algorithm, we propose a new algorithm to solve DCOP, which exhibits two important improvements over the DALO algorithm. First we use a sequential partial approach to select a coefficient of leaders to compute the best assignment for agents by which the computation and communication cost decrease in the whole DCOP. The second improvement is an evolutionary approach by which the computation and communication burden for each agent decreases. We present some empirical evidences that show our algorithm performs better than the DALO algorithm. key words distributed constraint optimization, multi agent system I
A Tutorial on Optimization for MultiAgent Systems
, 2012
"... Research on optimization in multiagent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multiagent application domains. Multiagent optimization focuses on casting MAS problems into optimization problems. The solving of those pro ..."
Abstract
 Add to MetaCart
(Show Context)
Research on optimization in multiagent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multiagent application domains. Multiagent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multiagent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multiagent optimization.