A core pursuit of artificial intelligence is the comprehension of human behavior. Imbuing intelligent agents with a good human behavior model can help them understand how to behave intelligently and interactively in complex situations. Due to the increase in data availability and computational resources, 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. Along with implementing a smart exploration strategy, the right representation for a task can help narrow the search process during learning. This dissertation tackles three important aspects of machine intelligence: 1) prediction, 2) exploration, and 3) representation. More specifically we develop new algorithms for 1) predicting the future maneuvers or outcomes in pilot training and computer architecture applications; 2) exploration strategies for reinforcement learning in game environments and 3) scene representations for autonomous driving agents capable of handling large numbers of dynamic entities. This dissertation makes the following research contributions in the area of representation learning. First, we introduce a new time series representation for flight trajectories in intelligent pilot training simulations. Second, we demonstrate a method, Temporally Aware Embedding (TAE) for learning an embedding that leverages temporal information extracted from data retrieval series. Third, the dissertation introduces GRAD (Graph Representation for Autonomous Driving) that incorporates the future location of neighboring vehicles into the decision-making process. We demonstrate the usage of our models for pilot training, cache usage prediction, and autonomous driving; however, believe that our new time series representations can be applied to many other types of modeling problems.


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Graduation Date





Sukthankar, Gita


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Computer Science

Degree Program

Computer Science


CFE0009632; DP0027666





Release Date

May 2023

Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)