Online feature elicitation in interactive optimization

Most models of utility elicitation in decision support and interactive optimization assume a predefined set of "catalog" features over which user preferences are expressed. However, users may differ in the features over which they are most comfortable expressing their preferences. In this work we consider the problem of feature elicitation: a user's utility function is expressed using features whose definitions (in terms of "catalog" features) are unknown. We cast this as a problem of concept learning, but whose goal is to identify only enough about the concept to enable a good decision to be recommended. We describe computational procedures for identifying optimal alternatives w.r.t. minimax regret in the presence of concept uncertainty; and describe several heuristic query strategies that focus on reduction of relevant concept uncertainty.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Craig Boutilier, Kevin Regan, Paolo Viappiani
• Keywords: Heuristic Query Strategies, ICML 2009, Machine Learning, Relevant Concept Uncertainty, Utility Elicitation