Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker - Martin Schmid
Don’t let the name fool you, “games” of imperfect information provide a general mathematical model that describes how decision-makers interact. AI research has a long history of using parlour games to study these models, but attention has been focused primarily on perfect information games, like checkers, chess or go. Poker is the quintessential game of imperfect information, where you and your opponent hold information that each other doesn't have (your cards).
DeepStack bridges the gap between AI techniques for games of perfect information—like checkers, chess and Go—with ones for imperfect information games–like poker–to reason while it plays using “intuition” honed through deep learning to reassess its strategy with each decision.DeepStack is the first theoretically sound application of heuristic search methods—which have been famously successful in games like checkers, chess, and Go—to imperfect information games.With a study published in Science in March 2017, DeepStack became the first AI capable of beating professional poker players at heads-up no-limit Texas hold'em poker.
Speaker: Martin Schmid
Research Scientist at Google DeepMind.Previously worked as a Research Scientist at IBM Watson.His research deals with game theory, machine learning and artificial intelligence.Co-author of DeepStack, the first AI to beat professional poker players at heads-up no-limit Texas hold'em poker.
• 18:00 The talk
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