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Knows what it knows: a framework for self-aware learning
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Source ICML; Vol. 307 archive
Proceedings of the 25th international conference on Machine learning table of contents
Helsinki, Finland
Pages 568-575  
Year of Publication: 2008
ISBN:978-1-60558-205-4
Authors
Lihong Li  Rutgers University, Piscataway, NJ
Michael L. Littman  Rutgers University, Piscataway, NJ
Thomas J. Walsh  Rutgers University, Piscataway, NJ
Sponsors
: Yahoo!
: Xerox
IBM : IBM
: NSF
Microsoft Research : Microsoft Research
: Machine Learning Journal/Springer
: Pascal
: University of Helsinki
: Federation of Finnish Learned Societies
: Intel Corporation
: Google
: Helsinki Institute for Information Technology
Publisher
ACM  New York, NY, USA
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ABSTRACT

We introduce a learning framework that combines elements of the well-known PAC and mistake-bound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is true in reinforcement-learning and active-learning problems. We catalog several KWIK-learnable classes and open problems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Bagnell, J., Ng, A. Y., & Schneider, J. (2001). Solving uncertain Markov decision problems (Technical Report CMU-RI-TR-01-25). Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
 
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Kakade, S., Kearns, M., & Langford, J. (2003). Exploration in metric state spaces. Proceedings of the 20th International Conference on Machine Learning.
 
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Kakade, S. M. (2003). On the sample complexity of reinforcement learning. Doctoral dissertation, Gatsby Computational Neuroscience Unit, University College London.
 
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Strehl, A. L., Diuk, C., & Littman, M. L. (2007). Efficient structure learning in factored-state MDPs. Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07).
 
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Strehl, A. L., & Littman, M. L. (2008). Online linear regression and its application to model-based reinforcement learning. Advances in Neural Information Processing Systems 20.
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Collaborative Colleagues:
Lihong Li: colleagues
Michael L. Littman: colleagues
Thomas J. Walsh: colleagues