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

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 ...

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate ...

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 ...

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 ...

Researchers at The University of Texas at Austin and Cognizant AI Labs have developed an AI-driven system that leverages 175 years of global land use and carbon storage data to generate optimal ...

Transfer Learning for Reinforcement Learning Domains: A Survey. Matthew E. Taylor and Peter Stone. Journal of Machine Learning Research, 10(1):1633–1685, 2009.

Transfer Learning for Reinforcement Learning on a Physical Robot. Samuel Barrett, Matt E. Taylor, and Peter Stone. In Ninth International Conference on Autonomous Agents and Multiagent Systems - ...

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 ...

Artificial Intelligence and Life in 2030. Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram ...

Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism. Kurt Dresner and Peter Stone. In The Third International Joint Conference on Autonomous Agents and Multiagent Systems ...