Neural Symbolic Machine Reasoning in the Physical World
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CitationYi, Kexin. 2021. Neural Symbolic Machine Reasoning in the Physical World. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.
AbstractThe ability to understand physics lies at the core of human intelligence. It has been a long-standing research goal in artificial intelligence to build algorithms able to perceive, explain and predict the physical world. The recent success of deep neural networks has triggered a surge of advancements in models and datasets based on visual observations. However, these efforts mainly focused on recognizing patterns, while physics understanding and reasoning remain underexplored. In this dissertation, I will discuss a series of works on physical reasoning beyond pattern recognition, via a hybrid approach that combines neural networks and symbolic representations. I will first discuss the application of the neural symbolic framework to visual question answering on static images. Then I will present a video dataset that focuses on dynamic and causal reasoning on simple physics experiments that involve classic object collision. A neural symbolic model that tackles these challenging tasks is also introduced. Next I will study dynamics modeling and prediction on more complex physical systems such as rigid bodies and deformable objects. Finally, I will conclude the dissertation by briefly discussing future directions of applying machine intelligence to open scientific questions.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37368350
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