Distributed belief averaging using sequential observations

Yang Liu; Ji Liu; Tamer Basar; Mingyan Liu

doi:10.23919/ACC.2017.7963031

Distributed belief averaging using sequential observations

Yang Liu, Ji Liu, Tamer Basar, Mingyan Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper considers a distributed belief averaging problem with sequential observations in which a group of n > 1 agents in a network, each having sequentially arriving samples of its belief in an online manner, aim to reach a consensus at the average of their beliefs, by exchanging information only with their neighbors. The neighbor relationships among the n agents are described by a time-varying undirected graph whose vertices correspond to agents and whose edges depict neighbor relationships. A distributed algorithm is proposed to solve this problem over sequential observations with O(1/t) convergence rate. Extensions to the case of directed graphs are also detailed.

Original language	English (US)
Title of host publication	2017 American Control Conference, ACC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	680-685
Number of pages	6
ISBN (Electronic)	9781509059928
DOIs	https://doi.org/10.23919/ACC.2017.7963031
State	Published - Jun 29 2017
Event	2017 American Control Conference, ACC 2017 - Seattle, United States Duration: May 24 2017 → May 26 2017

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Other

Other	2017 American Control Conference, ACC 2017
Country/Territory	United States
City	Seattle
Period	5/24/17 → 5/26/17

ASJC Scopus subject areas

Electrical and Electronic Engineering

Online availability

10.23919/ACC.2017.7963031

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Liu, Y, Liu, J, Basar, T & Liu, M 2017, Distributed belief averaging using sequential observations. in 2017 American Control Conference, ACC 2017., 7963031, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 680-685, 2017 American Control Conference, ACC 2017, Seattle, United States, 5/24/17. https://doi.org/10.23919/ACC.2017.7963031

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abstract = "This paper considers a distributed belief averaging problem with sequential observations in which a group of n > 1 agents in a network, each having sequentially arriving samples of its belief in an online manner, aim to reach a consensus at the average of their beliefs, by exchanging information only with their neighbors. The neighbor relationships among the n agents are described by a time-varying undirected graph whose vertices correspond to agents and whose edges depict neighbor relationships. A distributed algorithm is proposed to solve this problem over sequential observations with O(1/t) convergence rate. Extensions to the case of directed graphs are also detailed.",

author = "Yang Liu and Ji Liu and Tamer Basar and Mingyan Liu",

note = "Publisher Copyright: {\textcopyright} 2017 American Automatic Control Council (AACC).; 2017 American Control Conference, ACC 2017 ; Conference date: 24-05-2017 Through 26-05-2017",

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AB - This paper considers a distributed belief averaging problem with sequential observations in which a group of n > 1 agents in a network, each having sequentially arriving samples of its belief in an online manner, aim to reach a consensus at the average of their beliefs, by exchanging information only with their neighbors. The neighbor relationships among the n agents are described by a time-varying undirected graph whose vertices correspond to agents and whose edges depict neighbor relationships. A distributed algorithm is proposed to solve this problem over sequential observations with O(1/t) convergence rate. Extensions to the case of directed graphs are also detailed.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 24 May 2017 through 26 May 2017

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Distributed belief averaging using sequential observations

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