Distributed learning of wardrop equilibria

Dominique Barth; Olivier Bournez; Octave Boussaton; Johanne Cohen

doi:10.1007/978-3-540-85194-3_5

Distributed learning of wardrop equilibria

Dominique Barth
, Olivier Bournez
, Octave Boussaton
, Johanne Cohen

CNRS and PRiSM
LORIA Laboratoire Lorrain de Recherche en Informatique et ses Applications

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

Abstract

We consider the problem of learning equilibria in a well known game theoretic traffic model due to Wardrop. We consider a distributed learning algorithm that we prove to converge to equilibria. The proof of convergence is based on a differential equation governing the global macroscopic evolution of the system, inferred from the local microscopic evolutions of agents. We prove that the differential equation converges with the help of Lyapunov techniques.

Original language	English
Title of host publication	Unconventional Computation - 7th International Conference, UC 2008, Proceedings
Publisher	Springer Verlag
Pages	19-32
Number of pages	14
ISBN (Print)	3540851933, 9783540851936
DOIs	https://doi.org/10.1007/978-3-540-85194-3_5
Publication status	Published - 1 Jan 2008
Externally published	Yes
Event	7th International Conference on Unconventional Computation, UC 2008 - Vienna, Austria Duration: 25 Aug 2008 → 28 Aug 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5204 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Unconventional Computation, UC 2008
Country/Territory	Austria
City	Vienna
Period	25/08/08 → 28/08/08

Access to Document

10.1007/978-3-540-85194-3_5

Cite this

Barth, D., Bournez, O., Boussaton, O., & Cohen, J. (2008). Distributed learning of wardrop equilibria. In Unconventional Computation - 7th International Conference, UC 2008, Proceedings (pp. 19-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5204 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-85194-3_5

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title = "Distributed learning of wardrop equilibria",

abstract = "We consider the problem of learning equilibria in a well known game theoretic traffic model due to Wardrop. We consider a distributed learning algorithm that we prove to converge to equilibria. The proof of convergence is based on a differential equation governing the global macroscopic evolution of the system, inferred from the local microscopic evolutions of agents. We prove that the differential equation converges with the help of Lyapunov techniques.",

author = "Dominique Barth and Olivier Bournez and Octave Boussaton and Johanne Cohen",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Barth, D, Bournez, O, Boussaton, O & Cohen, J 2008, Distributed learning of wardrop equilibria. in Unconventional Computation - 7th International Conference, UC 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5204 LNCS, Springer Verlag, pp. 19-32, 7th International Conference on Unconventional Computation, UC 2008, Vienna, Austria, 25/08/08. https://doi.org/10.1007/978-3-540-85194-3_5

Distributed learning of wardrop equilibria. / Barth, Dominique; Bournez, Olivier; Boussaton, Octave et al.
Unconventional Computation - 7th International Conference, UC 2008, Proceedings. Springer Verlag, 2008. p. 19-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5204 LNCS).

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

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AU - Bournez, Olivier

AU - Boussaton, Octave

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N2 - We consider the problem of learning equilibria in a well known game theoretic traffic model due to Wardrop. We consider a distributed learning algorithm that we prove to converge to equilibria. The proof of convergence is based on a differential equation governing the global macroscopic evolution of the system, inferred from the local microscopic evolutions of agents. We prove that the differential equation converges with the help of Lyapunov techniques.

AB - We consider the problem of learning equilibria in a well known game theoretic traffic model due to Wardrop. We consider a distributed learning algorithm that we prove to converge to equilibria. The proof of convergence is based on a differential equation governing the global macroscopic evolution of the system, inferred from the local microscopic evolutions of agents. We prove that the differential equation converges with the help of Lyapunov techniques.

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Barth D, Bournez O, Boussaton O, Cohen J. Distributed learning of wardrop equilibria. In Unconventional Computation - 7th International Conference, UC 2008, Proceedings. Springer Verlag. 2008. p. 19-32. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-85194-3_5

Distributed learning of wardrop equilibria

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