Team for Research in
Ubiquitous Secure Technology

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

• Bestick_2013_10_TRUST.pdf · application/pdf · 1476 kbytes

• 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.
For additional information, see the Publications FAQ or contact webmaster at www truststc org.