Uncertainty in Artificial Intelligence
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Dynamic Teaching in Sequential Decision Making Environments
Thomas Walsh, Sergiu Goschin
Abstract:
We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimized learners that watch a teacher demonstrate a static policy, we focus on the teacher as a decision maker who can dynamically choose different policies to teach different parts of the environment. We develop several teaching frameworks based on previously defined supervised protocols, such as Teaching Dimension, extending them to handle noise and sequences of inputs encountered in an MDP.We provide theoretical bounds on the learnability of several important model classes in this setting and suggest a practical algorithm for dynamic teaching.
Keywords:
Pages: 863-872
PS Link:
PDF Link: /papers/12/p863-walsh.pdf
BibTex:
@INPROCEEDINGS{Walsh12,
AUTHOR = "Thomas Walsh and Sergiu Goschin",
TITLE = "Dynamic Teaching in Sequential Decision Making Environments",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "863--872"
}


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