In many situations, a set of hard constraints encodes the feasible configurations of some system or product over which users have preferences. We consider the problem of computing a best feasible solution when the user's utilities are partially known. Assuming bounds on utilities, efficient mixed integer linear programs are devised to compute the solution with minimax regret while exploiting generalized additive structure in a user's utility function. © Springer-Verlag 2003.
CITATION STYLE
Boutilier, C., Patrascu, R., Poupart, P., & Schuurmans, D. (2003). Constraint-based optimization with the minimax decision criterion. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2833, 168–182. https://doi.org/10.1007/978-3-540-45193-8_12
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