ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Fast planning through planning graph analysis
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Planning for temporally extended goals
Annals of Mathematics and Artificial Intelligence
Combining the Expressivity of UCPOP with the Efficiency of Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
The temporal logic of programs
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Planning with first-order temporally extended goals using heuristic search
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Simultaneous heuristic search for conjunctive subgoals
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Planning as satisfiability with preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Planning with goal utility dependencies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Preference-based web service composition: a middle ground between execution and search
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Specifying and computing preferred plans
Artificial Intelligence
Temporal accommodation of legal argumentation
Proceedings of the 13th International Conference on Artificial Intelligence and Law
Representing and reasoning with qualitative preferences for compositional systems
Journal of Artificial Intelligence Research
Computing infinite plans for LTL goals using a classical planner
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Preference-Based planning via MaxSAT
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Fair LTL synthesis for non-deterministic systems using strong cyclic planners
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In this paper we provide a technique for accomplishing this objective. Our technique can deal with a rich class of preferences, including so-called temporally extended preferences (TEPs). Unlike simple preferences which express desired properties of the final state achieved by a plan, TEPs can express desired properties of the entire sequence of states traversed by a plan, allowing the user to express a much richer set of preferences. Our technique involves converting a planning problem with TEPs into an equivalent planning problem containing only simple preferences. This conversion is accomplished by augmenting the inputed planning domain with a new set of predicates and actions for updating these predicates. We then provide a collection of new heuristics and a specialized search algorithm that can guide the planner towards preferred plans. Under some fairly general conditions our method is able to find a most preferred plan-i.e., an optimal plan. It can accomplish this without having to resort to admissible heuristics, which often perform poorly in practice. Nor does our technique require an assumption of restricted plan length or make-span. We have implemented our approach in the HPlan-P planning system and used it to compete in the 5th International Planning Competition, where it achieved distinguished performance in the Qualitative Preferences track.