Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Planning with goal utility dependencies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Soft goals can be compiled away
Journal of Artificial Intelligence Research
A look-ahead B&B search for cost-based planning
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
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Oversubscription planning (OSP) appears in many real problems where finding a plan achieving all goals is infeasible. The objective is to find a feasible plan reaching a goal subset while maximizing some measure of utility. In this paper, we present a new technique to select goals "a priori" for problems in which a cost bound prevents all the goals from being achieved. It uses estimations of distances between goals, which are computed using relaxed plans. Using these distances, a search in the space of subsets of goals is performed, yielding a new set of goals to plan for. A revised planning problem can be created and solved, taking into account only the selected goals. We present experiments in six different domains with good results.