Planning for conjunctive goals
Artificial Intelligence
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Block-Structured plan deordering
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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AI planners have to compromise between the speed of the planning process and the quality of the generated plan. Anytime planners try to balance these objectives by finding plans of better quality over time, but current anytime planners often do not make effective use of increasing runtime beyond a certain limit. We present a new method of continuing plan improvement, that works by repeatedly decomposing a given plan into subplans and optimising each subplan locally. The decomposition exploits block-structured plan deordering to identify coherent subplans, which make sense to treat as units. This approach extends the "anytime capability" of current planners - to provide continuing plan quality improvement at any time scale.