Modeling Interaction via the Principle of Maximum Causal Entropy

@inproceedings{Ziebart2010ModelingIV,
  title={Modeling Interaction via the Principle of Maximum Causal Entropy},
  author={Brian D. Ziebart and J. Andrew Bagnell and Anind K. Dey},
  booktitle={ICML},
  year={2010}
}
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distributions with elements of interaction and feedback where its applicability has not been established. This work presents the principle of maximum causal entropy – an approach based on causally conditioned probabilities that can appropriately model the availability and influence of sequentially revealed side information… CONTINUE READING
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Influence diagrams. Readings on the Principles and Applications of Decision Analysis (pp. 721–762)

  • R. A. Howard, J. E. Matheson
  • 1984
Highly Influential
5 Excerpts

When is a linear control system optimal

  • R. Kalman
  • Trans. ASME, J. Basic Engrg.,
  • 1964
Highly Influential
5 Excerpts

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