Convergence of Expected Utility for Universal AI
  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.
