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Using Short and Long Term Memory to induce environmental information from simple events*

Published online by Cambridge University Press:  09 March 2009

John L. Gordon ,
David Williams  and
Alan Hobson
John L. Gordon
Affiliation:
Department of Electrical and Electronic Engineering, Liverpool Polytechnic, Byrom Street, Liverpool L3 3AF (UK)
David Williams
Affiliation:
Department of Electrical and Electronic Engineering, Liverpool Polytechnic, Byrom Street, Liverpool L3 3AF (UK)
Alan Hobson
Affiliation:
Department of Electrical and Electronic Engineering, Liverpool Polytechnic, Byrom Street, Liverpool L3 3AF (UK)
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Summary

This paper considers the use of memory models and machine intelligence, to dynamically update a computer based representation of the occupancy of a small building. The input to the model is derived from very simple, single bit, movement sensors in each room of the premises.I It will be shown that the information derived from these sensors can provide adequate data for a building control scheme.

Short and Long Term memory models of man will be briefly reviewed. Working models for Short and Long Term memory will be discussed, which have evolved from the earlier work but which have been tuned to fit the machine level constraints of this type of application.

A review of the performance of a working pilot installation will be given. A performance measure will be derived and initial figures using this measure will be presented.

Keywords

Short-term memoryLong-term memoryEnvironmental informationMachine intelligenceBuilding occupancySensors
Type
Article
Information
Robotica , Volume 10 , Issue 1 , January 1992 , pp. 65 - 74
Copyright
Copyright © Cambridge University Press 1992

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References

1.Gordon, J.L., Williams, D. & Hobson, C.A., “Deriving complex location data from simple movement sensors” Robotica 8, part 2, 151–158 (1990).CrossRefGoogle Scholar
2.Winston, P.H., Artificial Intelligence, Second Edition (Addison-Wesley, UK, 1984) p. 201.Google Scholar
3.Narayanan, A., “Memory Models of Man & Machine” In: Artificial Intelligence, Principles and Actions. (Ed. Yazdani, Masoud), (Chapman & Hall, London, 1986) pp. 226–259.Google Scholar
4.Cohen, J., Complex Learning (Rand McNally, New York, 1969).Google Scholar
5.Miller, G.A., “The magical number seven, plus or minus two, Some limits on our capacity for processing information” Psychol Review 63, 81–97 (1956).CrossRefGoogle ScholarPubMed
6.Tulving, E., “Episodic and Semantic Memory” In: Organisation of Memory (Eds, Tulving, E. and Donaldson, W.) (Academic Press, New York, 1972).Google Scholar
7.Anderson, J.R., The Architecture of Cognition (Harvard University Press, Cambridge, MA, 1983).Google Scholar
8.Anderson, J.R., “A Theory of the Origins of Human Knowledge” In: Machine Learning Paradigms and Methods (Ed. Carbonell, Jamie) (MIT Press, Cambridge, MA, 1990) pp 313–351.Google Scholar