Super-Human AI for Strategic Reasoning

Опубликовано 14 сентября 2018, 14:32

Poker has been a challenge problem in game theory, operations research, and artificial intelligence for decades. As a game of imperfect information, it involves obstacles not present in games like chess and go, and requires totally different techniques. In 2017, our AI, Libratus, beat a team of four top specialist pros in the main benchmark for imperfect-information game solving, heads-up no-limit Texas hold'em, which has 10^161 decision points. This was the first time AI has beaten top players in a very large poker game. Libratus is powered by new algorithms in each of its three main modules: 1) computing approximate Nash equilibrium strategies before the event (i.e., computing a blueprint strategy for the entire game), 2) safe nested endgame solving during play (i.e., refining the blueprint strategy on the fly in parts of the game that are reached while preserving guarantees on exploitability), and 3) fixing its own strategy to play even closer to equilibrium based on what holes opponents have tried to identify and exploit. The algorithms are domain independent and have applicability to video games, strategic pricing, finance, negotiation, business strategy, strategic market segmentation, sports, investment banking, strategic product portfolio optimization, electricity markets, bidding, auction design, acquisition strategy (e.g., for streaming companies to acquire movies), political campaigns, cybersecurity, physical security, military, bot detection, and steering evolution and biological adaptation (such as for medical treatment planning and synthetic biology). The Libratus part of this talk is joint work with my PhD student Noam Brown.

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