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Diverse randomized agents vote to win

Authors: Xin Jiang, Albert; Soriano Marcolino, Leandro; Procaccia, Ariel D.; Sandholm, Tuomas; Shah, Nisarg; Tambe, Milind;

Diverse randomized agents vote to win

Abstract

We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the second-stage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.

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United Kingdom
27 references, page 1 of 3

[1] H. Azari Soufiani, W. Z. Chen, D. C. Parkes, and L. Xia. Generalized method-of-moments for rank aggregation. In Proc. of 27th NIPS, pages 2706-2714, 2013.

[2] H. Azari Soufiani, D. C. Parkes, and L. Xia. Random utility theory for social choice. In Proc. of 26th NIPS, pages 126-134, 2012.

[3] H. Azari Soufiani, D. C. Parkes, and L. Xia. Computing parametric ranking models via rank-breaking. In Proc. of 31st ICML, 2014. Forthcoming.

[4] P. Baudis˘ and J. l. Gailly. PACHI: State of the art open source go program. In Proc. of 13th ACG, pages 24-38, 2011.

[5] C. Boutilier, I. Caragiannis, S. Haber, T. Lu, A. D. Procaccia, and O. Sheffet. Optimal social choice functions: A utilitarian view. In Proc. of 13th EC, pages 197-214, 2012.

[6] Y. Braouezec. Committee, expert advice, and the weighted majority algorithm: An application to the pricing decision of a monopolist. Computational Economics, 35(3):245-267, 2010. [OpenAIRE]

[7] C. Browne, E. J. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton. A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1-43, 2012.

[8] I. Caragiannis, A. D. Procaccia, and N. Shah. When do noisy votes reveal the truth? In Proc. of 14th EC, pages 143-160, 2013.

[9] M. de Condorcet. Essai sur l'application de l'analyse a` la probabilite´ de de´cisions rendues a` la pluralite´ de voix. Imprimerie Royal, 1785. Facsimile published in 1972 by Chelsea Publishing Company, New York.

[10] M. Enzenberger, M. Mu¨ller, B. Arneson, and R. Segal. Fuego - an open-source framework for board games and Go engine based on Monte Carlo tree search. IEEE Transactions on Computational Intelligence and AI in Games, 2(4):259-270, 2010.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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Funded by
NSF| ICES: Small: Computational Fair Division: From Cake Cutting to Cloud Computing
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1215883
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Computing and Communication Foundations
,
NSF| CAREER: A Broad Synthesis of Artificial Intelligence and Social Choice
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1350598
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
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