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Universal Knowledge-Seeking Agents for Stochastic Environments.

ALGORITHMIC LEARNING THEORY (ALT 2013), (2013): 158.0-172.0

Cited by: 35|Views79
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Abstract

We define an optimal Bayesian knowledge-seeking agent, KL-KSA, designed for countable hypothesis classes of stochastic environments and whose goal is to gather as much information about the unknown world as possible. Although this agent works for arbitrary countable classes and priors, we focus on the especially interesting case where all...More

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