TY - THES
AU - Jiang, Xin
PY - 2011
TI - Representing and reasoning with large games
KW - Thesis/Dissertation
LA - eng
M3 - Text
AB - In the last decade, there has been much research at the interface of computer science and game theory. One important class of problems at this interface is the computation of solution
concepts (such as Nash equilibrium or correlated equilibrium) of a finite game. In order to take advantage of the highly-structured utility functions in games of practical interest, it
is important to design compact representations of games as well as efficient algorithms for computing solution concepts on such representations. In this thesis I present several novel contributions in this direction:
The design and analysis of Action-Graph Games (AGGs), a fully-expressive modeling language for representing simultaneous-move games.
We propose a polynomial-time algorithm for computing expected utilities given arbitrary mixed strategy profiles, and leverage the algorithm to achieve exponential speedups of existing algorithms for computing Nash equilibria. Designing efficient algorithms for computing pure-strategy Nash equilibria in AGGs. For symmetric AGGs with bounded treewidth our algorithm runs in polynomial time.
Extending the AGG framework beyond simultaneous-move games. We propose Temporal Action-Graph Games (TAGGs) for representing dynamic games and
Bayesian Action-Graph Games (BAGGs) for representing Bayesian games. For certain subclasses of TAGGs and BAGGs we gave efficient algorithms for equilibria that achieve exponential speedups over existing approaches.
Efficient computation of correlated equilibria. In a landmark paper, Papadimitriou and Roughgarden described a polynomial-time algorithm ("Ellipsoid Against Hope") for computing sample correlated equilibria of compactly-represented games. Recently, Stein, Parrilo and Ozdaglar showed that this algorithm can fail to find an exact correlated equilibrium. We present a variant of the Ellipsoid Against Hope algorithm that guarantees the polynomial-time identification of exact correlated equilibrium.
Efficient computation of optimal correlated equilibria. We show that the polynomial-time solvability of what we call the deviation-adjusted social welfare problem is a sufficient condition for the tractability of the optimal correlated equilibrium problem.
N2 - In the last decade, there has been much research at the interface of computer science and game theory. One important class of problems at this interface is the computation of solution
concepts (such as Nash equilibrium or correlated equilibrium) of a finite game. In order to take advantage of the highly-structured utility functions in games of practical interest, it
is important to design compact representations of games as well as efficient algorithms for computing solution concepts on such representations. In this thesis I present several novel contributions in this direction:
The design and analysis of Action-Graph Games (AGGs), a fully-expressive modeling language for representing simultaneous-move games.
We propose a polynomial-time algorithm for computing expected utilities given arbitrary mixed strategy profiles, and leverage the algorithm to achieve exponential speedups of existing algorithms for computing Nash equilibria. Designing efficient algorithms for computing pure-strategy Nash equilibria in AGGs. For symmetric AGGs with bounded treewidth our algorithm runs in polynomial time.
Extending the AGG framework beyond simultaneous-move games. We propose Temporal Action-Graph Games (TAGGs) for representing dynamic games and
Bayesian Action-Graph Games (BAGGs) for representing Bayesian games. For certain subclasses of TAGGs and BAGGs we gave efficient algorithms for equilibria that achieve exponential speedups over existing approaches.
Efficient computation of correlated equilibria. In a landmark paper, Papadimitriou and Roughgarden described a polynomial-time algorithm ("Ellipsoid Against Hope") for computing sample correlated equilibria of compactly-represented games. Recently, Stein, Parrilo and Ozdaglar showed that this algorithm can fail to find an exact correlated equilibrium. We present a variant of the Ellipsoid Against Hope algorithm that guarantees the polynomial-time identification of exact correlated equilibrium.
Efficient computation of optimal correlated equilibria. We show that the polynomial-time solvability of what we call the deviation-adjusted social welfare problem is a sufficient condition for the tractability of the optimal correlated equilibrium problem.
UR - https://open.library.ubc.ca/collections/24/items/1.0052175
ER - End of Reference