Probability Estimation in Face of Irrelevant Information
This addresses a foundational issue in designing learning agents for decision theory, though it appears incremental as it builds on existing statistical tools.
The paper tackles the problem of estimating probabilities for decision-making under uncertainty when agents have limited observations, showing that identifying relevant observations improves upon naive statistical methods.
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.