Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where re...

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Bibliographic Details
Main Author: Ziebart, Brian D.
Format: Others
Published: Research Showcase @ CMU 2010
Subjects:
Machine learning
decision making
probabilistic modeling
maximum entropy
inverse optimal control
influence diagrams
informational revelation
feedback
causality
goal inference
Online Access:http://repository.cmu.edu/dissertations/17
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1015&context=dissertations

Internet

http://repository.cmu.edu/dissertations/17
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1015&context=dissertations

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