MORSE: A general model to represent structured knowledge

In this paper, a model to represent knowledge (MORSE) that abstracts the structure of an automatic system is presented. This model is able to represent several ways of human reasoning, structured knowledge, execution strategies of an automatic system and many models presented in different works, such as hierarchical fuzzy controllers, cascade correlation neural networks architecture, decision trees, multilayer perceptrons, etc. Finally, thanks to the high level of abstraction of MORSE, the automatic systems specified by means of this model, have been classified in terms of their general features. This classification could allow a designer of systems to choose the best model of an automatic system to solve a problem. ©2000 John Wiley & Sons, Inc.

[1]  Witold Łukaszewicz.  Non-monotonic reasoning : formalization of commonsense reasoning , 1990 .

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  Jean-Pierre Nadal,et al.  Neural trees: a new tool for classification , 1990 .

[4]  Hiroyuki Watanabe,et al.  Application of a fuzzy discrimination analysis for diagnosis of valvular heart disease , 1994, IEEE Trans. Fuzzy Syst..

[5]  Hideyuki Takagi,et al.  Neural networks designed on approximate reasoning architecture and their applications , 1992, IEEE Trans. Neural Networks.

[6]  I. Tomek,et al.  Two Modifications of CNN , 1976 .

[7]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[8]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Dimitar Filev,et al.  Generation of Fuzzy Rules by Mountain Clustering , 1994, J. Intell. Fuzzy Syst..

[10]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[11]  A. Safiotti,et al.  Fuzzy logic in autonomous robotics: behavior coordination , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[12]  Hsin-Chia Fu,et al.  A divide-and-conquer methodology for modular supervised neural network design , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[13]  Alessandro Saffiotti,et al.  A Multivalued Logic Approach to Integrating Planning and Control , 1995, Artif. Intell..

[14]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[15]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[16]  Jack Sklansky,et al.  Automated design of linear tree classifiers , 1990, Pattern Recognit..

[17]  Florence d'Alché-Buc,et al.  Trio Learning: A New Strategy for Building Hybrid Neural Trees , 1994, Int. J. Neural Syst..

[18]  Kurt Konolige,et al.  Blending reactivity and goal-directedness in a fuzzy controller , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[19]  Juan Luis Castro,et al.  Non-monotonic fuzzy reasoning , 1998, Fuzzy Sets Syst..

[20]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[21]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[22]  Ignacio Requena,et al.  Are artificial neural networks black boxes? , 1997, IEEE Trans. Neural Networks.

[23]  Chuen-Tsai Sun,et al.  Rule-base structure identification in an adaptive-network-based fuzzy inference system , 1994, IEEE Trans. Fuzzy Syst..

[24]  Jun Zhou,et al.  Adaptive hierarchical fuzzy controller , 1993, IEEE Trans. Syst. Man Cybern..

[25]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[26]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..

[27]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[28]  Norman Abramson,et al.  Information theory and coding , 1963 .

[29]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.