This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the ...

The UT Programming Team consists of Trung Dang (coach) and teammates Aaryan Prakash, Mark Wen, and Dylan Smith from left to ...

As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without numerous high-cost ...

A UT Austin-led research team is developing AI-assisted tools to dramatically accelerate and simplify the design of radio frequency integrated circuits (RFICs)—a foundational technology for next-gen ...

Copyright © Gordon S. Novak Jr. Permission is granted for individuals to make copies of these notes for personal use, or for instructors to make copies for classroom ...

Hello! I'm Tristen Pool, a computer science student at the University of Texas at Austin. I’m drawn to the intricate world of machine learning, focusing on computer vision and natural language ...

Researchers at The University of Texas at Austin and Cognizant AI Labs have developed an AI-driven system that leverages 175 ...

Though computers have surpassed humans at many tasks, especially computationally intensive ones, there are many tasks for which human expertise remains necessary and/or useful. For such tasks, it is ...

Design and Optimization of an Omnidirectional Humanoid Walk:A Winning Approach at the RoboCup 2011 3D Simulation Competition. Patrick MacAlpine, Samuel Barrett, Daniel Urieli, Victor Vu, and Peter ...

Grounded Action Transformation for Robot Learning in Simulation. Josiah Hanna and Peter Stone. @InProceedings{AAAI17-Hanna, author = {Josiah Hanna and Peter Stone}, title = {Grounded Action ...