Probability Estimation in Face of Irrelevant Information

TL;DR

This paper presents a framework for agents to improve probability estimation under limited observations by identifying relevant information, enhancing decision-making under uncertainty.

Contribution

It introduces a method for determining relevant observations to improve probability estimation in decision-making agents, with extensions for incorporating additional knowledge.

Findings

Improved probability estimation by relevance determination

Framework for relevance-based observation selection

Extensions for leveraging extra knowledge

Abstract

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.