Referenced Items are not available for this document.
This paper presents an algorithm of unsupervised learning for applications in robotics and a knowledge structure which supports the behaviour generation (BG) module of the RCS/NASREM architecture designed at NIST. Minimum initial knowledge is presumed (“bootstrap knowledge”). The learning system uses the newly arrived information to extract rules of motion and construct a multiresolutional world model (WM). It evolves as a structure of knowledge representation which allows the BG to create and execute plans at each level of resolution. The concept of recursive generalization is explored as the main tool of rule extraction and knowledge organization. The experiment in learning is described based upon simulation of a 2D and a 3D mobile system
Published in:
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
(Volume:5
)
Date of Conference: 22-25 Oct 1995
- Page(s):
- 4404 - 4409 vol.5
- Meeting Date :
- 22 Oct 1995-25 Oct 1995
- Print ISBN:
- 0-7803-2559-1
- INSPEC Accession Number:
- 5144870
- Conference Location :
- Vancouver, BC
- Digital Object Identifier :
- 10.1109/ICSMC.1995.538487
- Product Type:
- Conference Publications
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