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arXiv · 0907.5598

Convergence of Expected Utility for Universal AI

Peter de Blanc
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Abstract

We consider a sequence of repeated interactions between an agent and an environment. Uncertainty about the environment is captured by a probability distribution over a space of hypotheses, which includes all computable functions. Given a utility function, we can evaluate the expected utility of any computational policy for interaction with the environment. After making some plausible assumptions (and maybe one not-so-plausible assumption), we show that if the utility function is unbounded, then the expected utility of any policy is undefined.

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Peter de Blanc. 2009-07-31. Convergence of Expected Utility for Universal AI. https://arxiv.org/abs/0907.5598

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