Game Theory in AIC

Players



old guys

Viliam Lisý

Branislav Bošanský

Tomáš Kroupa



trained guys

Vojtěch Kovařík

Karel Durkota

Pavel Rytíř



new blood

Karel Horák

Michal Šustr

Dominik Seitz

Petr Tomášek

Shuo Sun



in a galaxy far far away

Jiří Čermák (@Blindspot)

Jakub Černý (@NTU)

Karel Ha

Goals

... the goal is to win

  • Dynamic games with partial information (blind chess, poker, pursuit-evasion games, ...)
  • Security games (How to secure a computer network? Hackers are really smart ... )
  • Deception (How to deceive your opponent and not get fooled?)
  • Adversarial Machine Learning (Even a machine can learn ... be one step ahead!)
  • Coalitional Game Theory (feature importance using coalitional game theory)

Methods

... this is what we do

  • equilibrium computation algorithms for dynamic games (extensive-form games, stochastic games)
  • counterfactual regret minimization and continual resolving (DeepStack)
  • solving games with continuous action spaces
  • coalitional game theory

Tools

... every Jedi needs his light-saber

  • we prove stuff (showing theoretical guarantees and/or limitations of our algorithms)
  • we implement and experimentally validate our algorithms (Java, C++, TensorFlow)
  • domain-independent implementation of all our algorithms (hg clone http://jones.felk.cvut.cz/repo/gtlibrary)

Play a game

... work on difficult and interesting challenges

  • Bachelors/Masters projects
  • PhD positions
  • part-time positions

... collaborate with other researchers/industry

  • projects and active collaboration with Carnegie Mellon University, UTEP, University of Alberta/DeepMind, IST Austria
  • projects with TrendMicro, AVAST

Which game specifically can you play?

... only examples, you can propose your own

  • design an algorithm for the Scotland Yard board game (e.g., improve our domain-independent algorithm for this class of games)
  • design an algorithm for blind chess (Kriegspiel)
  • combine planners (e.g., A*) with game-theoretic algorithms (planning in an adversarial environment)
  • combine classifiers (e.g., simple image recognition, anomaly detection in computer network traffic) with game-theoretic algorithms (adversarial machine learning)
  • win over humans (build a mathematical model for human behavior for some game, design an algorithm that exploits human irrationality)
  • do the theory/math for combination of classifiers and game theory

Contact us: viliam.lisy@fel.cvut.cz bosansky@fel.cvut.cz