Constraint-Based Optimization with the Minimax Decision Criterion

  • Craig Boutilier
  • Relu Patrascu
  • Pascal Poupart
  • Dale Schuurmans
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2833)


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.


Utility Function Hard Constraint Feasible State Preference Elicitation Utility Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Craig Boutilier
    • 1
  • Relu Patrascu
    • 2
  • Pascal Poupart
    • 1
  • Dale Schuurmans
    • 2
  1. 1.Dept. of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.School of Computer ScienceUniversity of WaterlooWaterlooCanada

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