Nicholas K. Jong

Publications

Journal Articles

  • Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, and Mohamed N. Bennani. On the Use of Hybrid Reinforcement Learning for Autonomic Resource Allocation. Cluster Computing, 10(3):287-99, September 2007.
  • Peter Stone, Mohan Sridharan, Daniel Stronger, Gregory Kuhlmann, Nate Kohl, Peggy Fidelman, and Nicholas K. Jong. From Pixels to Multi-Robot Decision-Making: A Study in Uncertainty. Robotics and Autonomous Systems, 54(11):933-43, November 2006. Special issue on Planning Under Uncertainty in Robotics. [pdf]
  • Peter Stone, Kurt Dresner, Selim T. Erdogan, Peggy Fidelman, Nicholas K. Jong, Nate Kohl, Gregory Kuhlmann, Ellie Lin, Mohan Sridharan, Daniel Stronger, and Gurushyam Hariharan. UT Austin Villa 2003: A New RoboCup Four-Legged Team. In Daniel Polani, Brett Browning, Andrea Bonarini, and Kazuo Yoshida, editors, RoboCup-2003: Robot Soccer World Cup VII, Springer Verlag, Berlin, 2004. [pdf]

Conference Papers

  • Nicholas K. Jong and Peter Stone. Compositional Models for Reinforcement Learning. In Proceedings of the European Conference on Machine Learning and Principles and Pratice of Knowledge Discovery in Databases (ECML PKDD), September 2009. [pdf] [ps] [slides (pdf)]
  • Matthew E. Taylor, Nicholas K. Jong, and Peter Stone. Transferring Instances for Model-Based Reinforcement Learning. In Proceedings of the European Conference on Machine Learning and Principles and Pratice of Knowledge Discovery in Databases (ECML PKDD), September 2008. [pdf] [ps]
  • Nicholas K. Jong and Peter Stone. Hierarchical Model-Based Reinforcement Learning: R-max + MAXQ. In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008. [pdf] [ps] [slides (pdf)]
  • Nicholas K. Jong, Todd Hester, and Peter Stone. The Utility of Temporal Abstraction in Reinforcement Learning. In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008. [pdf] [ps] [slides (pdf)]
  • Nicholas K. Jong and Peter Stone. Model-Based Exploration in Continuous State Spaces. In The Seventh Symposium on Abstraction, Reformulation, and Approximation, July 2007. [pdf] [ps] [slides (pdf)]
  • Nicholas K. Jong and Peter Stone. Model-Based Function Approximation for Reinforcement Learning. In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007. [pdf] [ps] [slides (pdf)]
  • Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, and Mohamed N. Bennani. Improvement of Systems Management Policies Using Hybrid Reinforcement Learning. In Proceedings of the Seventeenth European Conference on Machine Learning (ECML), September 2006.
  • Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, and Mohamed N. Bennani. A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation. In Proceedings of the Third International Conference on Autonomic Computing (ICAC), June 2006. [pdf]
  • Nicholas K. Jong and Peter Stone. State Abstraction Discovery from Irrelevant State Variables. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, August 2005. [pdf]
  • Patrick Beeson, Nicholas K. Jong, and Benjamin Kuipers. Towards Autonomous Place Detection Using the Extended Voronoi Graph. In IEEE International Conference on Robotics and Automation, April 2005. [pdf]
  • Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, and Peter Stone. Learning Predictive State Representations. In Proceedings of the Twentieth International Conference on Machine Learning, August 2003. [pdf] [ps]

Workshop Papers

  • Gerald Tesauro, Rajarshi Das, and Nicholas K. Jong. Online Performance Management Using Hybrid Reinforcement Learning. In Proceedings of the First Workshop on Tackling Computer Systems Problems with Machine Learning Techniqies, June 2006.
  • Nicholas K. Jong and Peter Stone. Kernel-Based Models for Reinforcement Learning. In The ICML-2006 Workshop on Kernel Methods in Reinforcement Learning, June 2006. [pdf] [ps] [slides (pdf)]
  • Nicholas K. Jong and Peter Stone. Bayesian Models of Nonstationary Markov Decision Problems. In IJCAI 2005 workshop on Planning and Learning in A Priori Unknown or Dynamic Domains, August 2005. [pdf] [ps]
  • Nicholas K. Jong and Peter Stone. Towards Learning to Ignore Irrelevant State Variables. In The AAAI-2004 Workshop on Learning and Planning in Markov Processes — Advances and Challenges, July 2004. [pdf] [slides (pdf)]

Technical Reports

  • Nicholas K. Jong and Peter Stone. Towards Employing PSRs in a Continuous Domain. Technical Report UT-AI-TR-04-309, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2004. [pdf]
  • Peter Stone, Kurt Dresner, Peggy Fidelman, Nicholas K. Jong, Nate Kohl, Gregory Kuhlmann, Mohan Sridharan, Daniel Stronger. The UT Austin Villa 2004 RoboCup Four-Legged Team: Coming of Age. Technical Report UT-AI-TR-04-313, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2004. [pdf]