Title :
How hierarchies of objects and constraints reduce complexity
Author :
Iordanova, Blaga N.
Abstract :
Hierarchies help learning clearance-categories of flights and organise these knowledge categories in hierarchical structures of objects and constraints. They reduce complexity by introducing levels of decomposition in hierarchical layers of learning objects of clearance-categories and in satisfying constraints. They lower complexity by stressing parallelism in learning and in the formation of concepts of clearances in response to requests. They reduce the search problem into a decision problem.
Keywords :
air traffic control; computational complexity; constraint handling; decision theory; learning (artificial intelligence); search problems; air space-time; air traffic knowledge; clearance request; clearance-categories; complexity reduction; constraints; data structures; decomposition level; hierarchical structures; learning; object hierarchies; parallelism; search problem; Air traffic control; Aircraft; Automatic control; Control systems; Knowledge management; Neurons; Resource management; Search problems; Space technology; Technology management;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223403