DocumentCode
1855059
Title
Knowledge extraction from reinforcement learning
Author
Sun, Ron
Author_Institution
NEC Res. Inst., Princeton, NJ, USA
Volume
4
fYear
1999
fDate
1999
Firstpage
2554
Abstract
This paper deals with knowledge extraction from reinforcement learners. It addresses two approaches towards knowledge extraction: the extraction of explicit, symbolic rules front neural reinforcement learners; and the extraction of complete plans from such learners. The advantages of such knowledge extraction include: the improvement of learning (especially with the rule extraction approach); and the improvement of the usability of results of learning
Keywords
knowledge acquisition; learning (artificial intelligence); neural nets; symbol manipulation; knowledge extraction; neural networks; reinforcement learning; rule extraction; symbolic rules; Boltzmann distribution; Collaborative work; Decision making; Learning; National electric code; Neural networks; Stochastic processes; Sun; Usability;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.833476
Filename
833476
Link To Document