Regret-based Utility Elicitation in Constraint-based Decision Problems

Constraint-based optimization requires the formulation of a precise objective function. However, in many circumstances, the objective is to maximize the utility of a specific user among the space of feasible configurations (e.g., of some system or product). Since elicitation of utility functions is known to be difficult, we consider the problem of incremental utility elicitation in constraintbased settings. Assuming graphical utility models, and adopting the minimax regret decision criterion for optimization in the presence of imprecise utility, we describe several elicitation strategies that require the user to answer only binary (bound) queries on the utility model parameters. While a theoretically motivated algorithm can provably reduce regret quickly (in terms of number of queries), we demonstrate that, in practice, heuristic strategies perform much better, and are able to find optimal (or near-optimal) configurations with far fewer queries, while leaving much of the utility function unspecified.