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 generalizatio...
Gregg Rabideau, Barbara Engelhardt, Steve A. Chien
Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become in...
Generic SAT solvers have been very successful in solving hard combinatorial problems in various application areas, including AI planning. There is potential for improved performanc...
In this paper we show how CR-Prolog, a recent extension of A-Prolog, was used in the successor of USA-Advisor (USA-Smart) in order to improve the quality of the plans returned. The...
Most research in learning for planning has concentrated on efficiency gains. Another important goal is improving the quality of final plans. Learning to improve plan quality has b...