TY - GEN

T1 - Exploiting the structure of Distributed Constraint Optimization Problems (doctoral consortium)

AU - Fioretto, Ferdinando

PY - 2015

Y1 - 2015

N2 - In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost function. The use of DCOPs has gained momentum, due to their capability of addressing complex and naturally distributed problems. However, the adoption of DCOP on large problems faces two main limitations: (I) Modeling limitations, as current resolution methods detach the model from the resolution process, assuming that each agent controls a single variable of the problem; and (2) Solving capabilities, as the inability of current approaches to capitalize on the presence of structural information which may allow incoherent/unnecessary data to reticulate among the agents as well as to exploit structure of the agent's local problems. The purpose of the proposed dissertation is to address such limitations, studying how to adapt and integrate insights gained from centralized solving techniques in order to enhance DCOP performance and scalability, enabling their use for the resolution of real-world complex problems. To do so, we hypothesize that one can exploit the DCOP structure in both problem modeling and problem resolution phases.

AB - In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost function. The use of DCOPs has gained momentum, due to their capability of addressing complex and naturally distributed problems. However, the adoption of DCOP on large problems faces two main limitations: (I) Modeling limitations, as current resolution methods detach the model from the resolution process, assuming that each agent controls a single variable of the problem; and (2) Solving capabilities, as the inability of current approaches to capitalize on the presence of structural information which may allow incoherent/unnecessary data to reticulate among the agents as well as to exploit structure of the agent's local problems. The purpose of the proposed dissertation is to address such limitations, studying how to adapt and integrate insights gained from centralized solving techniques in order to enhance DCOP performance and scalability, enabling their use for the resolution of real-world complex problems. To do so, we hypothesize that one can exploit the DCOP structure in both problem modeling and problem resolution phases.

KW - CP

KW - DCOP

KW - Smart Grid

UR - http://www.scopus.com/inward/record.url?scp=84944705448&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84944705448&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84944705448

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 2007

EP - 2008

BT - AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems

A2 - Bordini, Rafael H.

A2 - Yolum, Pinar

A2 - Elkind, Edith

A2 - Weiss, Gerhard

PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

T2 - 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015

Y2 - 4 May 2015 through 8 May 2015

ER -