Cost-Optimal Symbolic Planning with State Trajectory and Preference Constraints
Stefan Edelkamp · European Conference on Artificial Intelligence · 2006
State trajectory and plan preference constraints are the two language features recently introduced to PDDL in the context of the 5th international planning competition. For planning with soft constraints, an objective function monitors their violation. This paper introduces a symbolic approach for finding cost-optimal plans. The set-based branch-and-bound algorithm exploits an efficient symbolic representation of the objective function. State trajectory constraints are compiled into automata, while ordinary preferences are evaluated on-line for the intersection of the search frontier with the goal.