Fast planning through planning graph analysis
Artificial Intelligence
S-MEP: A Planner for Numeric Goals
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
An approach to efficient planning with numerical fluents and multi-criteria plan quality
Artificial Intelligence
Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Generating plans in concurrent, probabilistic, over-subscribed domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Generating plans in concurrent, probabilistic, over-subscribed domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A belief-desire framework for goal revision
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
A hybrid LP-RPG heuristic for modelling numeric resource flows in planning
Journal of Artificial Intelligence Research
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By relaxing the hard-goal constraints from classical planning and associating them with reward values, over-subscription planning allows users to concentrate on presenting what they want and leaves the task of deciding the best goals to achieve to the planner. In this paper, we extend the over-subscription planning problem and its limited goal specification to allow numeric goals with continuous utility values and goals with mixed hard and soft constraints. Together they considerably extend the modeling power of goal specification and allow the user to express goal constraints that were not possible before. To handle these new goal constraints, we extend the Sapaps planner's planning graph based techniques to help it choose the best beneficial subset of goals that can include both hard or soft logical and numeric goals. We also provide empirical results in several benchmark domains to demonstrate that our technique helps return quality plans.