Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
on Advances in artificial intelligence
The complexity of robot motion planning
The complexity of robot motion planning
Case-based planning: viewing planning as a memory task
Case-based planning: viewing planning as a memory task
Efficient intersection tests for objects defined constructively
International Journal of Robotics Research
Artificial Intelligence - Special issue on knowledge representation
Analogical representation of space and time
Image and Vision Computing
Constraint-based reasoning
Interpreting a dynamic and uncertain world: task-based control
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Robot Motion Planning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Generation of Semantic Regions from Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Artificial Intelligence Review
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We report on a method that usesspatio-temporal reasoning to schedule aircraft to take-off positions.The objective is to aid human controllers to detect potentialconflicts which could cause hazards or delays. The challengeis to develop temporal-spatial reasoning systems that can handlecomplex and dynamic situations so that their results help thecontrollers to instruct the aircraft to move smoothly until theytake off. Optimisation is secondary and sometimes not easy tomeasure. Although the proposed method was developed with theaircraft domain in mind, it could be applied to order the movementsof interacting objects that have both expected paths and destinationtimes (i.e., ’’achieve this goal, at this place, at this time‘‘).