Abstract
An adaptive system called GBHCPR (Genetic Based Hierarchical Censored Production Rule) system based on Hierarchical Censored Production Rule (HCPR) system is presented that relies on development of some ties between Genetic Based Machine Learning (GBML) and symbolic machine learning. Several genetic operators are suggested that include advanced genetic operators, namely, Fusion and Fission. An appropriate credit apportionment scheme is developed that supports both forwardand backward chaining of reasoning process. A scheme for credit revision during the operationsof the genetic operators Fusion and Fission is also presented. A prototype implementation is included and experimental results are presented to demonstrate the performance of the proposed system.
On leave from the School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, INDIA-110067. The research of the first author is supported by the Brazilian foundation CNPq under Grant No. 301597/95-2.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
J.Bala, K.De Jong, and P.Pachowicz, “Using genetic algorithms to improve the performance of classification rules produced by symbolic inductive methods,” in Z.W.Ras and M.Zemankova (Eds.), Methodol., for Intelligent Systems, 6th Inter. Symposuim, Charlotte, N.C., USA, pp 286–295, 1991.
K.K.Bharadwaj and N.K.Jain, “Hierarchical Censored Production Rules (HCPRs) System,” Data and Knowledge Engineering, vol.8, (North Holland), pp 19–34, 1992.
K.K.Bharadwaj and RenuVarshneya “Parallelization of Hierarchical Censored Production Rules (HCPRs) System” Information and Software Technology, Butterworth-Heinemann(UK), vol. 27, No.8, pp453–460, 1995.
K.A.De Jong, “Genetic-algorithms based learning,” in Y.Kodraloff and R.S.Michalski (Eds.), Mchine Learning: An Artificial Intelligence Approach, vol.3, Morgan Kaufmann Publishers, Los Altos, CA, pp 611–637, 1990.
S.Forrest, “A study of parallelism in the classifier system and its applications to classification in KL-ONE semantic networks,” Ph.D. dissertation, Ann arbor, University of Michigan, 1985.
D.E.Goldberg, “Genetic-based machine learning: whence it came,where it's going,” in M.S.Elzas, T.I.Oren, and B.P.Zeiglar, Eds. Model. and Simul. Methodol.,knowledge systems paradigms, Elsevier Science Publishers B.V. (North Holland), 1989.
J.Grefestette, “A system for learning control strategies with genetic algorithms,” 3rd Inter. Conf. on Genetic Algorithms, Fairfax, VA. Morgan Kaufmann Publishers, pp 183–190, 1989.
N.M. Hewahi, Genetic Algorithm approach for Adaptive Hierarchical Censored Production Rule-Based System, Ph.D dissertation, School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India, 1994.
J.H.Holland, “Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems,” in R.S.Michalski, J.G.Carbonell, and T.M.Mitchell, Eds. Machine Learning: An Artificial Intelligence Approach, 1986.
J.H.Holland, “Adaptation in natural and artificial systems,” Ann Arbor: The University of Michigan press, 1975.
J.R.Koza, “Genetic programming on programming of computers by means of natural selection”, MIT, 1992.
R.S.Michalski and P.H.Winston, “Variable Precision Logic,” Artificial Intelligene, vol.29, pp 121–145, North Holland, 1986.
C.P.Stackhouse and B.P.Zeiglar, “Learning plateaus in adaptive Rule-Based system,” in M.S.Elzas, T.I.Oren, and B.P.Zeiglar, Eds. Model. and Simul. Methodol., Knowledge Systems' paradigms, Elsevier Science Publishers B.V.(North Holland), pp 359–379, 1989.
T.H.Westerdale, “A reward scheme for production systems with overlapping conflict sets,” IEEE Trans. on Sys., Man,and Cyber., vol. SMC-16, no.3, pp 369–383, May/June 1986.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bharadwaj, K.K., Hewahi, N.M., Brandao, M.A. (1996). Adaptive Hierarchical Censored Production Rule-based system: A genetic algorithm approach. In: Borges, D.L., Kaestner, C.A.A. (eds) Advances in Artificial Intelligence. SBIA 1996. Lecture Notes in Computer Science, vol 1159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61859-7_9
Download citation
DOI: https://doi.org/10.1007/3-540-61859-7_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61859-1
Online ISBN: 978-3-540-70742-4
eBook Packages: Springer Book Archive