Deriving an optimally deceptive policy in two-player iterated games

Elisabeth Paulson, Booz Allen Hamilton, Christopher Griffin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We formulate the problem of determining an optimally deceptive strategy in a repeated game framework. We assume that two players are engaged in repeated play. During an initial time period, Player 1 may deceptively train his opponent to expect a specific strategy. The opponent computes a best response. The best response is computed on an optimally deceptive strategy that maximizes the first player's long-run payoff during actual game play. Player 1 must take into consideration not only his real payoff but also the cost of deception. We formulate the deception problem as a nonlinear optimization problem and show how a genetic algorithm can be used to compute an optimally deceptive play. In particular, we show how the cost of deception can lead to strategies that blend a target strategy (policy) and an optimally deceptive one.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3808-3813
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Paulson, E., Hamilton, B. A., & Griffin, C. (2016). Deriving an optimally deceptive policy in two-player iterated games. In 2016 American Control Conference, ACC 2016 (pp. 3808-3813). [7525506] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7525506