The elicitation of preferences from individual users has been an increasing area of research. The goal is to learn as much as possible about a user's values (utilities) for different outcomes in order to perform tasks on behalf of the user. The aim of this report is to build on existing work on assisting agents in choosing the most effective query to pose to a user. The report will explore different ways of using the estimated utility values of each outcome in a preference graph to calculate the expected value of each possible query. Our simulation results will be evaluated using three evaluation measures: the number of new preferences actually learned as a result of the query chosen, the number of pairs of outcomes for which the true preference is correctly predicted by the new estimated utilities, and the root mean squared error between the estimated utilities and the true utilities. The performance of some of the methods implemented (Default and Normal-0. 05) were quite competitive with the already existing Minimum method. However, the other methods implemented performed poorly when compared with the existing method.
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