Utility Extraction

Utility extraction is the semi-automatic acquisition of decision maker's preferences about the different outcomes of a decision problem.

Research has focused on three different areas:

• eliciting the utility function based on a database of already elicited utility functions;
• iterative refinement of the decision maker’s current utility function using a value of information approach;
• eliciting the utility function based on a database of observed behavioral patterns.

The last approach implies that preferences are reflected in the behavior, and that the decision maker is behavioral consistent. As real-world behaviors and decisions are often not consistent, methods based on such assumptions can extract only trivial utility functions. Thomas D. Nielsen and Finn V. Jensen (Learning a decision maker’s utility function from (possibly) inconsistent behavior) proposed two algorithms that can take into account inconsistent behaviors, in order to reflect human preferences in real contexts.

Further Reading & References

• A brief tutorial on preferences in AI by Luke Muehlhauser
• Learning a decision maker’s utility function from (possibly) inconsistent behavior by Thomas D. Nielsen and Finn V. Jensen

See also

• Utility function
• Decision theory