February 28, 2019
Outperforming humans in a well-defined intellectually challenging task, which the humans spent decades practicing and studying, is a clear sign of intelligence.
Outperforming humans in a well-defined intellectually challenging task, which the humans spent decades practicing and studying, is a clear sign of intelligence. Therefore, outperforming professional players of checkers, chess, backgammon, go and poker have been important milestones in artificial intelligence research. Solving large games is also very useful in practical applications, for example in physical and network security.
In this talk, I will briefly introduce the key AI methods behind computing strategies in chess and Go. Then we will focus on imperfect information games, where players do not have the same information about the state of the game. Approximating optimal strategies for these games is fundamentally more difficult and simple adaptations of the techniques from perfect information games do not lead to good performance. I will explain the algorithm we developed for DeepStack, the first computer program that outperformed human professionals in no-limit Texas hold’em poker. Evaluating the performance of bots in such a complex game presents interesting challenges and I will explain how we overcame them. Finally, I will talk about the limitations of the algorithm used by DeepStack and future research directions inspired by these limitations.
Viliam Lisý is an assistant professor at the Artificial Intelligence Center, Department of Computer Science, FEE, Czech Technical University in Prague. Previously, he was a postdoctoral fellow working with Michael Bowling at University of Alberta in Canada. He has received his Ph.D. from CTU in Prague and holds M.Sc. degrees from the Free University of Amsterdam (VUA) and Charles University in Prague. During his studies, he worked at Carnegie Mellon University, Ben Gurion University and the Phillips Innovation Labs. Viliam’s research interests include sequential imperfect-information games and applications of game theory in real-world problems. He co-leads the Game Theory Group at the Artificial Intelligence Center.
Its program consists of a one-hour lecture followed by a discussion. The lecture is based on an (internationally) exceptional or remarkable achievement of the lecturer, presented in a way which is comprehensible and interesting to a broad computer science community. The lectures are in English.
The seminar is organized by the organizational committee consisting of Roman Barták (Charles University, Faculty of Mathematics and Physics), Jaroslav Hlinka (Czech Academy of Sciences, Computer Science Institute), Michal Chytil, Pavel Kordík (CTU in Prague, Faculty of Information Technologies), Michal Koucký (Charles University, Faculty of Mathematics and Physics), Jan Kybic (CTU in Prague, Faculty of Electrical Engineering), Michal Pěchouček (CTU in Prague, Faculty of Electrical Engineering), Jiří Sgall (Charles University, Faculty of Mathematics and Physics), Vojtěch Svátek (University of Economics, Faculty of Informatics and Statistics), Michal Šorel (Czech Academy of Sciences, Institute of Information Theory and Automation), Tomáš Werner (CTU in Prague, Faculty of Electrical Engineering), and Filip Železný (CTU in Prague, Faculty of Electrical Engineering)
The idea to organize this seminar emerged in discussions of the representatives of several research institutes on how to avoid the undesired fragmentation of the Czech computer science community.