Team for Research in
Ubiquitous Secure Technology

An Inverse Correlated Equilibrium Framework for Utility Learning in Multiplayer, Noncooperative Settings
Aaron Bestick

Citation
Aaron Bestick. "An Inverse Correlated Equilibrium Framework for Utility Learning in Multiplayer, Noncooperative Settings". Talk or presentation, 10, October, 2013.

Abstract
In a game-theoretic framework, given parametric agent utility functions, we solve the inverse problem of computing the feasible set of utility function parameters for each individual agent, given that they play a correlated equilibrium strategy. We model agents as utility maximizers, then cast the problem of computing the parameters of players' utility functions as a linear program using the fact that their play results in a correlated equilibrium. We focus on situations where agents must make tradeoffs between multiple competing components within their utility function. We test our method first on a simulated game of Chicken-Dare, and then on data collected in a real-world trial of a mobile fitness game in which five players must balance between protecting their privacy and receiving a reward for burning calories and improving their physical fitness. Through the learned utility functions from the fitness game, we hope to gain insight into the relative importance each user places on safeguarding their privacy vs. achieving the other desirable objectives in the game.

Electronic downloads

Citation formats  
  • HTML
    Aaron Bestick. <a
    href="http://www.truststc.org/pubs/925.html"
    ><i>An Inverse Correlated Equilibrium Framework for
    Utility Learning in Multiplayer, Noncooperative
    Settings</i></a>, Talk or presentation,  10,
    October, 2013.
  • Plain text
    Aaron Bestick. "An Inverse Correlated Equilibrium
    Framework for Utility Learning in Multiplayer,
    Noncooperative Settings". Talk or presentation,  10,
    October, 2013.
  • BibTeX
    @presentation{Bestick13_InverseCorrelatedEquilibriumFrameworkForUtilityLearning,
        author = {Aaron Bestick},
        title = {An Inverse Correlated Equilibrium Framework for
                  Utility Learning in Multiplayer, Noncooperative
                  Settings},
        day = {10},
        month = {October},
        year = {2013},
        abstract = {In a game-theoretic framework, given parametric
                  agent utility functions, we solve the inverse
                  problem of computing the feasible set of utility
                  function parameters for each individual agent,
                  given that they play a correlated equilibrium
                  strategy. We model agents as utility maximizers,
                  then cast the problem of computing the parameters
                  of players' utility functions as a linear program
                  using the fact that their play results in a
                  correlated equilibrium. We focus on situations
                  where agents must make tradeoffs between multiple
                  competing components within their utility
                  function. We test our method first on a simulated
                  game of Chicken-Dare, and then on data collected
                  in a real-world trial of a mobile fitness game in
                  which five players must balance between protecting
                  their privacy and receiving a reward for burning
                  calories and improving their physical fitness.
                  Through the learned utility functions from the
                  fitness game, we hope to gain insight into the
                  relative importance each user places on
                  safeguarding their privacy vs. achieving the other
                  desirable objectives in the game.},
        URL = {http://www.truststc.org/pubs/925.html}
    }
    

Posted by Carolyn Winter on 18 Nov 2013.
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