research-article

ER-DCOPs: A Framework for Distributed Constraint Optimization with Uncertainty in Constraint Utilities

Authors:

Ferdinando Fioretto,

Enrico PontelliAuthors Info & Claims

AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems

Pages 606 - 614

Published: 09 May 2016 Publication History

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.

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Cited By

Leite AEnembreck F(2019)COOPTJournal of Artificial Intelligence Research10.1613/jair.1.1146864:1(987-1023)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11468
Fioretto FYeoh W(2018)AI buzzwords explainedAI Matters10.1145/3175502.31755063:4(8-13)Online publication date: 16-Feb-2018
https://dl.acm.org/doi/10.1145/3175502.3175506
Fioretto FPontelli EYeoh WDechter R(2018)Accelerating exact and approximate inference for (distributed) discrete optimization with GPUsConstraints10.1007/s10601-017-9274-123:1(1-43)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10601-017-9274-1

Index Terms

ER-DCOPs: A Framework for Distributed Constraint Optimization with Uncertainty in Constraint Utilities
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Mathematics of computing
  1. Probability and statistics
    1. Stochastic processes

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cover image ACM Other conferences

AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems

May 2016

1580 pages

ISBN:9781450342391

General Chairs:
Catholijn M. Jonker
TU Delft, Netherlands
,
Stacy Marsella
University of Southern California, USA
,
Program Chairs:
John Thangarajah
RMIT University. Australia
,
Karl Tuyls
University of Liverpool, UK

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IFAAMAS

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 09 May 2016

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National Science Foundation

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AAMAS '16

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AAMAS '16: International Conference on Agents and Multiagent Systems

May 9 - 13, 2016

Singapore, Singapore

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AAMAS '16 Paper Acceptance Rate 137 of 550 submissions, 25%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

Leite AEnembreck F(2019)COOPTJournal of Artificial Intelligence Research10.1613/jair.1.1146864:1(987-1023)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11468
Fioretto FYeoh W(2018)AI buzzwords explainedAI Matters10.1145/3175502.31755063:4(8-13)Online publication date: 16-Feb-2018
https://dl.acm.org/doi/10.1145/3175502.3175506
Fioretto FPontelli EYeoh WDechter R(2018)Accelerating exact and approximate inference for (distributed) discrete optimization with GPUsConstraints10.1007/s10601-017-9274-123:1(1-43)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10601-017-9274-1

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