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Autonomous Discovery of Abstractions through Interaction with an Environment

  • Amy McGovern
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2371)

Abstract

The ability to create and use abstractions in complex environments, that is, to systematically ignore irrelevant details, is a key reason that humans are effective problem solvers. My research focuses on using machine learning techniques to enable greater autonomy in agents. I am particularly interested in autonomous methods for identifying and creating multiple types of abstractions from an agent’s accumulated experience in interacting with its environment. Specific areas of interest include:
  • Knowledge representation. How can we efficiently represent the knowledge learned in one task and reuse it for other tasks? This knowledge can take the form of a control policy learned to solve one task or a representation of structure in an environment.

  • Autonomous discovery of structure. My dissertation [1] focuses on autonomously identifying and creating useful temporal abstractions from an agent’s interaction with its environment.

  • Interaction of reinforcement learning and supervised learning methods. I am particularly interested in the combined use of these techniques to create more robust and autonomous learning systems.

  • Application of these techniques to robots, with a particular focus on robots assisting a human presence in space.

Keywords

Reinforcement Learning Learning Agent Effective Problem Solver Supervise Learning Method Temporal Abstraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Amy McGovern. Autonomous Discovery of Temporal Abstractions from Interaction with an Environment. PhD thesis, University of Massachusetts Amherst, 2002.Google Scholar
  2. 2.
    Amy McGovern and Andrew G. Barto. Accelerating reinforcement learning through the discovery of useful subgoals. In Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space: i-SAIRAS 2001, page electronically published, 2001.Google Scholar
  3. 3.
    Amy McGovern and Andrew G. Barto. Automatic discovery of subgoals in reinforcement learning using diverse density. In C. Brodley and A. Danyluk, editors, Proceedings of the 18th International Conference on Machine Learning ICML 2001, pages 361–368, San Francisco, CA, 2001. Morgan Kaufmann Publishers.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Amy McGovern
    • 1
  1. 1.Computer Science DepartmentUniversity of Massachusetts AmherstAmherst

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