Cooperative learning in multi-agent systems from intermittent measurements

Naomi Ehrich Leonard; Alex Olshevsky

doi:10.1109/CDC.2013.6761079

Cooperative learning in multi-agent systems from intermittent measurements

Naomi Ehrich Leonard, Alex Olshevsky

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

5 Scopus citations

Abstract

Motivated by the problem of decentralized direction-tracking, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector μ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of μ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) network connecting the nodes.

Original language	English (US)
Title of host publication	2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7492-7497
Number of pages	6
ISBN (Print)	9781467357173
DOIs	https://doi.org/10.1109/CDC.2013.6761079
State	Published - 2013
Event	52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy Duration: Dec 10 2013 → Dec 13 2013

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Other

Other	52nd IEEE Conference on Decision and Control, CDC 2013
Country/Territory	Italy
City	Florence
Period	12/10/13 → 12/13/13

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC.2013.6761079

Cite this

Leonard, N. E., & Olshevsky, A. (2013). Cooperative learning in multi-agent systems from intermittent measurements. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013 (pp. 7492-7497). Article 6761079 (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2013.6761079

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title = "Cooperative learning in multi-agent systems from intermittent measurements",

abstract = "Motivated by the problem of decentralized direction-tracking, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector μ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of μ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) network connecting the nodes.",

author = "Leonard, {Naomi Ehrich} and Alex Olshevsky",

year = "2013",

doi = "10.1109/CDC.2013.6761079",

language = "English (US)",

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series = "Proceedings of the IEEE Conference on Decision and Control",

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

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address = "United States",

note = "52nd IEEE Conference on Decision and Control, CDC 2013 ; Conference date: 10-12-2013 Through 13-12-2013",

}

Leonard, NE & Olshevsky, A 2013, Cooperative learning in multi-agent systems from intermittent measurements. in 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013., 6761079, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 7492-7497, 52nd IEEE Conference on Decision and Control, CDC 2013, Florence, Italy, 12/10/13. https://doi.org/10.1109/CDC.2013.6761079

Cooperative learning in multi-agent systems from intermittent measurements. / Leonard, Naomi Ehrich; Olshevsky, Alex.
2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 7492-7497 6761079 (Proceedings of the IEEE Conference on Decision and Control).

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

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T1 - Cooperative learning in multi-agent systems from intermittent measurements

AU - Leonard, Naomi Ehrich

AU - Olshevsky, Alex

PY - 2013

Y1 - 2013

N2 - Motivated by the problem of decentralized direction-tracking, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector μ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of μ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) network connecting the nodes.

AB - Motivated by the problem of decentralized direction-tracking, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector μ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of μ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) network connecting the nodes.

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EP - 7497

BT - 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 52nd IEEE Conference on Decision and Control, CDC 2013

Y2 - 10 December 2013 through 13 December 2013

ER -

Cooperative learning in multi-agent systems from intermittent measurements

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All Science Journal Classification (ASJC) codes

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