Heterogeneous knowledge representation integrating connectionist and symbolic computation

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摘要

Heterogeneous knowledge representation allows the combination of several knowledge representation techniques. For instance connectionist and symbolic systems are two different computational paradigms and knowledge representations. Unfortunately, the integration of different paradigms and knowledge representations is not easy and very often is informal. In this paper, we propose a formal approach to integrate these two paradigms where as a symbolic system we consider a (logic) rule-based system. The integration is operated at language level between neural networks and rule languages. The formal model that allows the integration is based on constraint logic programming and provides an integrated framework to represent and process heterogeneous knowledge. In order to achieve this we define a new language that allows us to express and model in a natural and intuitive way the above issues together with the operational semantics.

论文关键词:Artificial neural networks,Logic programming,Integration

论文评审过程:Received 10 March 1997, Available online 19 May 1998.

论文官网地址:https://doi.org/10.1016/S0950-7051(97)00016-6