A connectionist production system with approximate matching function

A hybrid symbolic/connectionist implementation of a production system with an approximate matching function is presented. The authors demonstrate the conversion of sequential production rules into a parallel firing connectionist network consisting of simple cells and providing an approximate matching function. The hybrid symbolic/connectionist production system (HSC-PS) supports two types of working memory element, the attribute/value pair and the object/attribute/value triplet. An attribute may be either crisp or fuzzy. The degree of match is measured for a condition having a fuzzy attribute and the working memory element which exists for that attribute and is represented by a value between 0 and 1. The strength of firing for a rule is decided by the minimum match of all the conditions in the left hand side of the rule. The strength of firing affects the actual amount of action to be performed on a fuzzy attribute. If two or more actions are to be performed on the same fuzzy attribute, a new action is composed from them and executed. A classic control problem is taken as an example and the results of the HSC-PS are compared with those of a fuzzy logic controller.<<ETX>>

[1]  Nils J. Nilsson,et al.  Problem-solving methods in artificial intelligence , 1971, McGraw-Hill computer science series.

[2]  Lawrence O. Hall,et al.  A hybrid/symbolic connectionist production system , 1992, Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92.

[3]  James G. Schmolze,et al.  A Parallel Asynchronous Distributed Production System , 1990, AAAI.

[4]  白井 良明,et al.  Artificial intelligence : concepts, techniques, and applications , 1984 .

[5]  H. Simon,et al.  Computer simulation of human thinking and problem solving. , 1962, Monographs of the Society for Research in Child Development.

[6]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

[7]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

[8]  S. G. Romaniuk,et al.  Fuzzy connectionist expert systems , 1989, International 1989 Joint Conference on Neural Networks.

[9]  T. Samad Towards connectionist rule-based systems , 1988, IEEE 1988 International Conference on Neural Networks.

[10]  William Mettrey,et al.  A comparative evaluation of expert system tools , 1991, Computer.

[11]  Dan I. Moldovan,et al.  Performance Comparison of Models for Multiple Rule Firing , 1991, IJCAI.

[12]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.