Jefferson Provost
Intelligent Robotics Lab: Ph.D. Alumni
Neural Networks Lab: Ph.D. Alumni
I study learning in robots and other situated, embodied agents. This interest includes both scaling up existing learning algorithms to the rich, high-dimensional, continuous sensorimotor systems of such agents, and integrating learning and adaptation into their general decision-making processes. My research seeks to understand how an agent with a rich, realistic sensorimotor system can learn to perceive, act, and achieve a broad range of goals, with minimal prior knowledge of itself and its world. My dissertation presents Self-Organizing Distinctive-state Abstraction (SODA), a generic method by which a robot in a continuous world can learn a set of high-level perceptual features and temporally-extended actions for navigating in large environments. Using SODA an agent has learned to perform a navigation task requiring hundreds small-scale, local actions using as few as nine new, temporally-extended actions, significantly improving learning time over navigating with local actions. My dissertation advisors are Ben Kuipers and Risto Miikkulainen.
Reinforcement Learning in High-Diameter, Continuous Environments 2007
Jefferson Provost, PhD Thesis, Computer Sciences Department, University of Texas at Austin.
Self-Organizing Distinctive State Abstraction Using Options 2007
Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen, In Proceedings of the 7th International Conference on Epigenetic Robotics 2007.
Developing navigation behavior through self-organizing distinctive state abstraction 2006
Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen, Connection Science, Vol. 18 (2006), pp. 159-172.
Bootstrap Learning of Foundational Representations. 2005
Benjamin Kuipers, Patrick Beeson, Joseph Modayil and Jefferson Provost, In Developmental Robotics, AAAI Spring Symposium Series 2005.
Self-Organizing Perceptual and Temporal Abstraction for Robot Reinforcement Learning 2004
Jefferson Provost, Benjamin J. Kuipers and Risto Miikkulainen, In AAAI-04 Workshop on Learning and Planning in Markov Processes 2004.
Modeling Cortical Maps with Topographica 2004
James A. Bednar, Yoonsuck Choe, Judah De Paula, Risto Miikkulainen, Jefferson Provost, and Tal Tversky, Neurocomputing (2004), pp. 1129-1135.
Exploiting local perceptual models for topological map-building 2003
Patrick Beeson, Matt MacMahon, Joseph Modayil, Jefferson Provost, Francesco Savelli and Benjamin Kuipers, In IJCAI-2003 Workshop on Reasoning with Uncertainty in Robotics (RUR-03) 2003.
Learning from uninterpreted experience in the SSH 2001
Benjamin Kuipers, Patrick Beeson, Joseph Modayil and Jefferson Provost, In AAAI Spring Symposium Series, Learning Grounded Representations, Stanford, CA 2001.
Toward Learning the Causal Layer of the Spatial Semantic Hierarchy using SOMs 2001
Jefferson Provost, Patrick Beeson, and Benjamin J. Kuipers, In AAAI Spring Symposium Series, Learning Grounded Representations 2001.
LISSOM

The LISSOM package contains the C++, Python, and Scheme source code and examples for training and testing firing-rate...

2001

Formerly affiliated with Neural Networks Formerly affiliated with Intelligent Robotics