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Viliam Lisý Learning to play large imperfect-information games

28 Feb 2019   16:15-18:15

The fortieth meeting of the Prague computer science seminar

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.

Posluchárna S5, MFF UK Malostranské nám. 25, Praha 1
Katedra kybernetiky FEL ČVUT
Contact person
Mgr. Helena Houšková
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