UTCS Artificial Intelligence
In supervised learning the desired outputs are known for each input, and the task is to learn a mapping between them that generalizes well to new inputs.
Former Ph.D. Student
elie [at] cs utexas edu
Former Ph.D. Student
Alan J. Lockett
alan lockett [at] gmail com
risto [at] cs utexas edu
kstanley [at] cs ucf edu
tansey [at] cs utexas edu
austin [at] cs utexas edu
GRADE: Machine Learning Support for Graduate Admissions
Austin Waters, Risto Miikkulainen, To Appear In
Innovative Applications in Artificial Intelligence
Accelerating Evolution via Egalitarian Social Learning
Wesley Tansey, Eliana Feasley, and Risto Miikkulainen, In
Proceedings of the 14th Annual Genetic and Evolutionary Computation Conference (GECCO 2012)
, Philadelphia, Pennsylvania, USA, July 2012.
Temporal Convolution Machines for Sequence Learning
Alan J Lockett and Risto Miikkulainen, Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin, 2009.
Detecting Motion in the Environment with a Moving Quadruped Robot
Peggy Fidelman, Thayne Coffman and Risto Miikkulainen, In Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi, editors,
RoboCup-2006: Robot Soccer World Cup X
, 219-231, Berlin, 2007. Springer Verlag.
Learning Concept Drift with a Committee of Decision Trees
Kenneth O. Stanley, Technical Report AI03-302, Department of Computer Sciences, The University of Texas at Austin, 2003.
Controlling Search for the Consequences of New Information during Knowledge Integration
K. Murray and Bruce Porter , In
Proceedings of the Sixth International Workshop on Machine Learning
, 290-295, Ithaca, NY, June 1989.
Teaching an Agent Manually via Evaluative Reinforcement (TAMER)
2008 - Present
1998 - 1998
Data Rectification for Process Control
1992 - 1992
This is the C# source code for the experiments with Egalitarian Social Learning (ESL) in a robot foraging domain. The re...
Egalitarian Social Learning (ESL) in Robot Foraging
Knowledge Representation & Reasoning