Abstract
Neurules are a type of hybrid rules combining a symbolic and a connectionist representation. There are two disadvantages of neurules. The first is that the created neurule bases usually contain multiple representations of the same piece of knowledge. Also, the inference mechanism is rather connectionism oriented than symbolism oriented, thus reducing naturalness. To remedy these deficiencies, we introduce an extension to neurules, called multineurules, and an alternative inference process, which is rather symbolism oriented. Experimental results comparing the two inference processes are also presented.
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References
Medsker L.R.: Hybrid Neural Networks and Expert Systems. Kluwer Academic Publishers, Boston (1994)
Tirri H.: Replacing the Pattern Matcher of an Expert System with a Neural Network. In:Goonatilake S., Sukdev K. (eds): Intelligent Hybrid Systems. John Wiley & Sons (1995).
Gallant, S.I.: Neural Network Learning and Expert Systems. MIT Press (1993)
Hatzilygeroudis, I., Prentzas, J.: Neurules: Improving the Performance of Symbolic Rules. International Journal on AI Tools (IJAIT) 9(1), (2000) 113–130
Hatzilygeroudis, I., Prentzas, J.: Constructing Modular Hybrid Knowledge Bases for Expert Systems. International Journal on AI Tools (IJAIT) 10(1–2) (2001) 87–105
Hatzilygeroudis, I., Prentzas, J.: An Efficient Hybrid Rule Based Inference Engine with Explanation Capability. Proceedings of the 14th International FLAIRS Conference, Key West, FL. AAAI Press (2001) 227–231
Fu L.M.: Neural Networks in Computer Intelligence. McGraw-Hill (1994)
Hatzilygeroudis, I., Vassilakos, P. J., Tsakalidis, A.: XBONE: A Hybrid Expert System for Supporting Diagnosis of Bone Diseases. In: Pappas, C., Maglaveras, N., Scherrert J.-R. (eds): Medical Informatics Europe’97 (MIE’97). IOS Press (1997) 295–
Ghalwash, A.Z.: A Recency Inference Engine for Connectionist Knowledge Bases. Applied Intelligence 9 (1998) 201–215
Hatzilygeroudis I., Prentzas, J.: HYMES: A HYbrid Modular Expert System with Efficient Inference and Explanation. Proceedings of the 8th Panhellenic Conference on Informatics, Nicosia, Cyprus, Vol.1 (2001) 422–431
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Hatzilygeroudis, I., Prentzas, J. (2002). Multi-inference with Multi-neurules. In: Vlahavas, I.P., Spyropoulos, C.D. (eds) Methods and Applications of Artificial Intelligence. SETN 2002. Lecture Notes in Computer Science(), vol 2308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46014-4_4
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DOI: https://doi.org/10.1007/3-540-46014-4_4
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