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个人简介
Ph.D. in Computer Science, University of Michigan (2010)
M.S. in Computer Science, University of Michigan (2007)
B.A. in Computer Science and Mathematics, Oberlin College (2004)
My primary research interests focus on the core artificial intelligence ambition of creating artifical autonomous agents that can behave flexibly and competently in rich, complex environments. I tend to approach this problem through my background in machine learning, and specifically reinforcement learning. Broadly speaking, then, I focus on the learning problem faced by an agent that is placed in an unknown environment and would like to learn, from its own experience, how to make good decisions (where "good" is defined by some specified reward signal). This raises interesting and challenging questions about how such an agent should represent and maintain knowledge in order to make both learning and planning tractible, even when the world is very complex. Beyond this primary focus, I am also quite broadly interested in machine learning in general as a tool for turning the ever-growing mountain of available data into useful computational artifacts.
Papers
Agnostic System Identification for Monte Carlo Planning. Erik Talvitie. In 'Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI),' 2015. (pdf) (bibtex)
Improving Exploration in UCT Using Local Manifolds. Sriram Srinivasan, Erik Talvitie, and Michael Bowling. In 'Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI),' 2015. (pdf) (bibtex)
Policy Tree: Adaptive Representation for Policy Gradient. Ujjwal Das Gupta, Erik Talvitie, and Michael Bowling. In 'Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI),' 2015. (pdf) (bibtex)
Model Regularization for Stable Sample Rollouts. Erik Talvitie. In 'Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI),' 2014. (pdf) (bibtex)
Skip Context Tree Switching. Marc Bellemare, Joel Veness, and Erik Talvitie. In 'Proceedings of the Thirty-First International Conference on Machine Learning (ICML),' 2014. (pdf) (bibtex)
研究兴趣
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Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligencepp.5573-5577, (2018)
THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCEno. 1 (2017): 2597-2603
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AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligencepp.2986-2992, (2015)
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Proceedings of the AAAI Conference on Artificial Intelligenceno. 1 (2015)
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