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

Electrical Engineering

Stony Brook University

Harvard University
University of Illinois at Urbana-Champaign
University of Michigan, Ann Arbor

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

2 Scopus citations

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
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

Conference

Conference	2017 American Control Conference, ACC 2017
Country/Territory	United States
City	Seattle
Period	05/24/17 → 05/26/17

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10.23919/ACC.2017.7963031

Cite this

@inproceedings{28565d1170ca41b7bcf0304a8b1dc56d,

title = "Distributed belief averaging using sequential observations",

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",

year = "2017",

month = jun,

day = "29",

doi = "10.23919/ACC.2017.7963031",

language = "English",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "680--685",

booktitle = "2017 American Control Conference, ACC 2017",

}

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, 05/24/17. https://doi.org/10.23919/ACC.2017.7963031

Distributed belief averaging using sequential observations. / Liu, Yang; Liu, Ji; Basar, Tamer et al.
2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 680-685 7963031 (Proceedings of the American Control Conference).

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

TY - GEN

T1 - Distributed belief averaging using sequential observations

AU - Liu, Yang

AU - Liu, Ji

AU - Basar, Tamer

AU - Liu, Mingyan

PY - 2017/6/29

Y1 - 2017/6/29

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/85027051961

U2 - 10.23919/ACC.2017.7963031

DO - 10.23919/ACC.2017.7963031

M3 - Conference contribution

T3 - Proceedings of the American Control Conference

SP - 680

EP - 685

BT - 2017 American Control Conference, ACC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 American Control Conference, ACC 2017

Y2 - 24 May 2017 through 26 May 2017

ER -

Distributed belief averaging using sequential observations

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