Announcing the Final Examination of Zerong Xi for the degree of Doctor of Philosophy
A core pursuit of artificial intelligence is the comprehension of human behavior, as well as understanding how to behave intelligently and interactively in complex situations. Benefiting from the growth of computational resources and large datasets, the development of machine learning algorithms for duplicating human cognitive abilities has made rapid progress. To solve difficult scenarios, learning-based methods must search for solutions in a predefined but large space
using optimization techniques with a hint of data. This flexibility is particularly helpful for modeling complicated environments, especially ones involving interacting with humans or multiple agents. Meanwhile, it imposes challenges on developing representations and algorithms that can adapt to the environment.
Committee in Charge: Gita Sukthankar, Liqiang Wang, Ladislau Boloni, Matthew E. Taylor
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