Dr. Gerry Tesauro is a Research Staff Member in computer science at the IBM TJ Watson Research Center in Hawthorne, NY. He is famous for developing TD-Gammon a self-teaching program that learned to play backgammon at human world championship level. TD-Gammon uses reinforcement learning — a well known technique in Machine Learning that has a close connection to the role of dopamine neurons in the brain.
Gerry was invited to Almaden by Nimrod Megiddo, and gave us a wonderful talk on how he is applying reinforcement learning to improving system management policies.
References:
1. Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mohamed N. Bennani: Improvement of Systems Management Policies Using Hybrid Reinforcement Learning. ECML 2006: 783-791
2. Gerald Tesauro: Online Resource Allocation Using Decompositional Reinforcement Learning. AAAI 2005: 886-891
3. Gerald Tesauro: Temporal Difference Learning and TD-Gammon. Commun. ACM 38(3): 58-68 (1995)