Learning a decision maker's utility function from (possibly) inconsistent behavior
Artificial Intelligence
Learning a decision maker's utility function from (possibly) inconsistent behavior
Artificial Intelligence
Computers and Electronics in Agriculture
Variable elimination for influence diagrams with super value nodes
International Journal of Approximate Reasoning
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Hi-index | 0.00 |
The influence diagram framework serves as a powerful modeling tool for symmetric decision problems with a single decision maker. However, one of the main difficulties when representing decision problems using influence diagrams is eliciting the utilities and the probabilities. This makes it desirable to be able to investigate: 1) how sensitive the solution is to variations in some utility or probability parameter, and 2) how robust the solution is to joint variations over a set of parameters. In this paper, we propose a general algorithm for performing these types of analysis.