Learning IPD strategies through co-evolution

Siang Yew Chong; Jan Humble; Graham Kendall; Jiawei Li; Xin Yao

doi:10.1142/9789812770684_0003

Learning IPD strategies through co-evolution

Siang Yew Chong, Jan Humble, Graham Kendall, Jiawei Li, Xin Yao

School of Computer Science

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

4 Citations (Scopus)

Abstract

The following sections are included: • Introduction • Co-evolving Strategies for the IPD Game Co-evolutionary Learning Framework Shadow of the Future Issues for Co-evolutionary Learning of IPD Strategies • Extending the IPD Game Extending the IPD with More Choices IPD with Noise N-Player IPD Other Extensions • Conclusion and Future Directions • References.

Original language	English
Title of host publication	Iterated Prisoners’ Dilemma, The
Subtitle of host publication	20 Years On
Publisher	World Scientific Publishing Co.
Pages	63-87
Number of pages	25
ISBN (Electronic)	9789812770684
DOIs	https://doi.org/10.1142/9789812770684_0003
Publication status	Published - 1 Jan 2007

ASJC Scopus subject areas

General Mathematics
General Computer Science

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10.1142/9789812770684_0003

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title = "Learning IPD strategies through co-evolution",

abstract = "The following sections are included: • Introduction • Co-evolving Strategies for the IPD Game Co-evolutionary Learning Framework Shadow of the Future Issues for Co-evolutionary Learning of IPD Strategies • Extending the IPD Game Extending the IPD with More Choices IPD with Noise N-Player IPD Other Extensions • Conclusion and Future Directions • References.",

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AU - Kendall, Graham

AU - Li, Jiawei

AU - Yao, Xin

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N2 - The following sections are included: • Introduction • Co-evolving Strategies for the IPD Game Co-evolutionary Learning Framework Shadow of the Future Issues for Co-evolutionary Learning of IPD Strategies • Extending the IPD Game Extending the IPD with More Choices IPD with Noise N-Player IPD Other Extensions • Conclusion and Future Directions • References.

AB - The following sections are included: • Introduction • Co-evolving Strategies for the IPD Game Co-evolutionary Learning Framework Shadow of the Future Issues for Co-evolutionary Learning of IPD Strategies • Extending the IPD Game Extending the IPD with More Choices IPD with Noise N-Player IPD Other Extensions • Conclusion and Future Directions • References.

UR - https://www.scopus.com/pages/publications/84867715290

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DO - 10.1142/9789812770684_0003

M3 - Book Chapter

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BT - Iterated Prisoners’ Dilemma, The

PB - World Scientific Publishing Co.

ER -

Learning IPD strategies through co-evolution

Abstract

ASJC Scopus subject areas

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