DocumentCode :
3052195
Title :
Improving tactical plans with genetic algorithms
Author :
Schultz, Alan C. ; Grefenstette, John J.
Author_Institution :
US Naval Res. Lab., Washington, DC, USA
fYear :
1990
fDate :
6-9 Nov 1990
Firstpage :
328
Lastpage :
334
Abstract :
The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid a missile. The learning method relies on the notion of competition and uses genetic algorithms to search the space of decision policies. In the research presented here, the use of available heuristic domain knowledge to initialize the population to produce better plans is investigated
Keywords :
aerospace simulation; genetic algorithms; learning systems; planning (artificial intelligence); flight simulator; genetic algorithms; heuristic domain knowledge; learning decision rules; sequential; tactical plans; Animation; Artificial intelligence; Decision making; Delay; Genetic algorithms; Laboratories; Learning systems; Machine learning; Missiles; Pipelines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools for Artificial Intelligence, 1990.,Proceedings of the 2nd International IEEE Conference on
Conference_Location :
Herndon, VA
Print_ISBN :
0-8186-2084-6
Type :
conf
DOI :
10.1109/TAI.1990.130358
Filename :
130358
Link To Document :
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