Integration of hierarchical censored production rule (HCPR)-based system and neural networks

Over the past few years, researchers have successfully developed a number of systems that combine the strength of the symbolic and connectionist approaches to artificial intelligence. Most of the efforts have employed standard production rules, IF
THEN as underlying symbolic representation. This paper is an attempt towards integrating hierarchical censored production rule based system and neural networks. A HCPR has the form: decision (if, precondition) (unless, censor conditions) (generality, general information) (specificity, specific information) which can be made to exhibit variable precision in the reasoning such that both certainty of belief in a conclusion and its specificity may be controlled by the reasoning process. The proposed hybrid system would have numerous applications where decision must be taken in real time and with uncertain information.