C4.5: programs for machine learning
C4.5: programs for machine learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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This paper proposes a systematic approach of representing abstract features in terms of low-level, subjective state representations. We demonstrate that a mapping between the agent's predictive state representation and abstract features can be derived automatically from high-level training data supplied by the designer. Our empirical evaluation demonstrates that an experience-oriented state representation built around a single-bit sensor can represent useful abstract features such as "back against a wall", "in a corner", or "in a room". As a result, the agent gains virtual sensors that could be used by its control policy.