Uncertainty in Artificial Intelligence
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Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium
Greg Hines, Kate Larson
Abstract:
We study a multiagent learning problem where agents can either learn via repeated interactions, or can follow the advice of a mediator who suggests possible actions to take. We present an algorithmthat each agent can use so that, with high probability, they can verify whether or not the mediator's advice is useful. In particular, if the mediator's advice is useful then agents will reach a correlated equilibrium, but if the mediator's advice is not useful, then agents are not harmed by using our test, and can fall back to their original learning algorithm. We then generalize our algorithm and show that in the limit it always correctly verifies the mediator's advice.
Keywords: null
Pages: 274-281
PS Link:
PDF Link: /papers/08/p274-hines.pdf
BibTex:
@INPROCEEDINGS{Hines08,
AUTHOR = "Greg Hines and Kate Larson",
TITLE = "Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "274--281"
}


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