Optimal Solution Stability in Dynamic, Distributed Constraint Optimization

Petcu, Adrian; Faltings, Boi

doi:10.1109/iat.2007.11

Cited by 29 publications

(23 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The worst case runtime, communication, and memory requirements of these two algorithms to solve the DCOP at each time step are the same as those of DPOP, as they use DPOP as a subroutine 7. The full name of the algorithm was not provided by Petcu and Faltings (2007b). to update the Q-and R-values. The exception is that agents in the Distributed Q-learning algorithm also broadcast their value assignments at each time step to all other agents.…”

Section: Rs-dpopmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

143

119

View full text Add to dashboard Cite

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments.This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

show abstract

Section: Rs-dpopmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

143

119

View full text Add to dashboard Cite

show abstract

“…However, in case the problems have highly induced width, the messages generated in the high-width areas of the problem become too large. There have been proposed a number of variations of this algorithm that address this problem and other issues, offering various tradeoffs (see Petcu and Faltings 2005a, 2005b, 2005c, 2005d, 2005e, 2005f, 2006. Petcu and Faltings (2005a) propose an approximated version of this algorithm, which allows the desired tradeoff between solution quality and computational complexity.…”

Section: Privacy-preserving Collaborative Optimization: Theoretical Mmentioning

confidence: 99%

A Survey of Privacy-Aware Supply Chain Collaboration: From Theory to Applications

Yuan

Vaidya

Wang

2013

Journal of Information Systems

View full text Add to dashboard Cite

In the contemporary information era, the ubiquitous collection of data from different parties frequently accommodates significant mutual benefits to the involved participants. However, data is a double-bladed sword. Inappropriate access or use of data by the recipients may pose serious privacy issues that explicitly harm the data owners. In the past decade, swiftly increasing privacy concerns arise in many business processes such as supply chain management. How to protect the private information of different participants in the supply chain has become a key multidisciplinary research problem in information systems, production and operations management, computer science, and mathematics. Specifically, in the real world, manufacturers, distributors, and retailers commonly collaborate with each other to cater to the demands of supplying and marketing. In their traditional cooperation, all the parties completely share their proprietary information so as to jointly optimize their operations (e.g., maximize their profit or minimize their cost). Now, they realize that completely sharing such information would bring considerable negative impact to themselves. For overcoming this, some recent research results begin to make the following ideal occasion possible—all the participants collaboratively solve a realistic problem without revealing any private proprietary information to each other. In this paper, we primarily review the literature on the applications of privacy-preserving techniques to supply chain collaboration among multiple parties. We first identify various private proprietary information required in the supply chain collaboration, and discuss several potential privacy-preserving techniques. Then, we review the relevant research results from theory to applications. Since intensive collaboration in modern supply chains opens even more opportunities in both academia and industry, we finally outline the future research trend and the potential challenges in this promising area.

show abstract

“…This is extended to map over-constrained problems into DCOP but can handle only static problems as the author concedes to the lack of an effective DCOP algorithm for dynamic problems. Petcu et al [10] [11] propose S-DPOP and RS-DPOP, which utilize self stabilizing DFS trees to guarantees optimal solution stability in distributed continuous-time combinatorial optimization problems. Lass et al [12] deal with the complicating factor of dynamism by wrapping ADOPT in an Adapter that receives and handles dynamic event requests.…”

Section: Solving Dynamic Complex Problemsmentioning

confidence: 99%

An Efficient Algorithm for Solving Dynamic Complex DCOP Problems

Khanna

Sattar

Hansen

et al. 2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

View full text Add to dashboard Cite

Multi Agent Systems and the Distributed Constraint Optimization Problem (DCOP) formalism offer several asynchronous and optimal algorithms for solving naturally distributed optimization problems efficiently. There has been good application of this technology in addressing real world problems in areas like Sensor Networks and Meeting Scheduling. Most of these algorithms however exploit static tree structures and are thus not well suited to modeling and solving problems in rapidly changing domains. Also, while in theory most DCOP algorithms can be extended to handle complex local sub-problems, we argue that this generally results in making their performance sub-optimal, and thus their application less suitable. In this paper we present new measures that emphasize the interconnectedness between each agent's local and interagent sub-problems and use these measures to guide dynamic agent ordering during distributed constraint reasoning. The resulting algorithm, DCDCOP, offers a robust, flexible, and efficient mechanism for modeling and solving dynamic complex problems. Experimental evaluation of the algorithm shows that DCDCOP significantly outperforms ADOPT, the gold standard in search-based DCOP algorithms.

show abstract

Optimal Solution Stability in Dynamic, Distributed Constraint Optimization

Cited by 29 publications

References 6 publications

Distributed Constraint Optimization Problems and Applications: A Survey

Distributed Constraint Optimization Problems and Applications: A Survey

A Survey of Privacy-Aware Supply Chain Collaboration: From Theory to Applications

An Efficient Algorithm for Solving Dynamic Complex DCOP Problems

Contact Info

Product

Resources

About