Using Generic Preferences to Incrementally Improve Plan Quality

We describe a methodology for representing and optimizing user preferences on plans. Our approach differs from previous work on plan optimization in that we employ a generalization of commonly occurring plan quality metrics, providing an expressive preference language. We introduce a domain independent algorithm for incrementally improving the quality of feasible plans with respect to preferences described in this language. Finally, we experimentally show that plan quality can be significantly increased with little additional modeling effort for each domain.

• Type: Conference
• Year: 2000
• Where: AIPS
• Authors: Gregg Rabideau, Barbara Engelhardt, Steve A. Chien
• Keywords: AIPS 2000, Artificial Intelligence, Expressive Preference Language, Plan Quality, Plan Quality Metrics