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Brief announcement: achieving reliability in master-worker computing via evolutionary dynamics

Published: 16 July 2012 Publication History

Abstract

This work considers Internet-based task computations in which a master process assigns tasks, over the Internet, to rational workers and collect their responses. The objective is for the master to obtain the correct task outcomes. For this purpose we formulate and study the dynamics of evolution of Internet-based master-worker computations through reinforcement learning.

References

[1]
C. F. Camerer. Behavioral game theory: Experiments in strategic interaction. Roundtable Series in Behavioral Economics, 2003.
[2]
J. Shneidman and D. C. Parkes. Rationality and self-interest in P2P networks. In IPTPS 2003, pp. 139--148.
[3]
C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool publishers, 2010.
[4]
Technical report of this work: %TR-12-02, Dept. of Computer Science, University of Cyprus, February 2012.http://www.cs.ucy.ac.cy/~chryssis/EvolMW-TR.pdf

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cover image ACM Conferences
PODC '12: Proceedings of the 2012 ACM symposium on Principles of distributed computing
July 2012
410 pages
ISBN:9781450314503
DOI:10.1145/2332432

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 July 2012

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Author Tags

  1. algorithmic mechanism design
  2. evolutionary dynamics
  3. internet-based task computing
  4. reinforcement learning

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