Probability Estimation in Face of Irrelevant Information
  In this paper, we consider one aspect of the problem of applying decision
theory to the design of agents that learn how to make decisions under
uncertainty. This aspect concerns how an agent can estimate probabilities for
the possible states of the world, given that it only makes limited observations
before committing to a decision. We show that the naive application of
statistical tools can be improved upon if the agent can determine which of his
observations are truly relevant to the estimation problem at hand. We give a
framework in which such determinations can be made, and define an estimation
procedure to use them. Our framework also suggests several extensions, which
show how additional knowledge can be used to improve tile estimation procedure
still further.
