Safe and Nested Subgame Solving for Imperfect-Information Games with Tuomas Sandholm – NIPS Best Paper ’17

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In this episode, I speak with Tuomas Sandholm, Carnegie Mellon University Professor and, along with his PhD student Noam Brown, winner of a 2017 NIPS Best Paper award for the paper “Safe and Nested Subgame Solving for Imperfect-Information Games.”

Tuomas and I dig into the significance of the paper, including a breakdown of perfect vs imperfect information games, the role of abstractions in game solving, and how the concept of safety applies to gameplay. We discuss how all these elements and techniques are applied to poker, and how the algorithm described in this paper was used by Noam and Tuomas to create Libratus, the first AI to beat top human pros in No Limit Texas Hold’em, a particularly difficult game to beat due to its large state space. This was a fascinating interview that I’m really excited to share with you all. Enjoy!

This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration.

About Toumas

Mentioned in the Interview

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