@inproceedings{f72a2de56d674c86a21f4d4f572b753d,
title = "ER-DCOPs: A framework for distributed constraint optimization with uncertainty in constraint utilities",
abstract = "Distributed Constraint Optimization Problems (DCOPs) have been used to model a number of multi-agent coordination problems. In DCOPs, agents are assumed to have complete information about the utility of their possible actions. However, in many real-world applications, such utilities are stochastic due to the presence of exogenous events that are beyond the direct control of the agents. This paper addresses this issue by extending the standard DCOP model to Expected Regret DCOP (ER-DCOP) for DCOP applications with uncertainty in constraint utilities. Different from other approaches, ER-DCOPs aim at minimizing the overall expected regret of the problem. The paper proposes the ER-DPOP algorithm for solving ER-DCOPs, which is complete and requires a linear number of messages with respect to the number of agents in the problem. We further present two implementations of ER-DPOP-GPU- and ASP-based implementations-that orthogonally exploit the problem structure and present their evaluations on random networks and power network problems.",
keywords = "ASP, DCOP, Expected regret, GPU",
author = "Tiep Le and Ferdinando Fioretto and William Yeoh and Son, {Tran Cao} and Enrico Pontelli",
note = "Publisher Copyright: Copyright {\textcopyright} 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 ; Conference date: 09-05-2016 Through 13-05-2016",
year = "2016",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "606--614",
booktitle = "AAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems",
}