Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems

Ioannis Hatzilygeroudis, Jim Prentzas
2005 International Journal of Hybrid Intelligent Systems  
In this paper, we first present and compare existing categorization schemes for neuro-symbolic approaches. We then stress the point that not all hybrid neuro-symbolic approaches can be accommodated by existing categories. Such a case is rule-based neuro-symbolic approaches that propose a unified knowledge representation scheme suitable for use in expert systems. That kind of integrated schemes have the two component approaches tightly and indistinguishably integrated, offer an interactive
more » ... nce engine and can provide explanations. Therefore, we introduce a new category of neuro-symbolic integrations, namely 'representational integrations'. Furthermore, two sub-categories of representational integrations are distinguished, based on which of the two component approaches of the integrations is given pre-eminence. Representative approaches as well as advantages and disadvantages of both sub-categories are discussed. Keywords: Neuro-symbolic integrations, Rule-based expert systems, Connectionist expert systems * The order is alphabetical Various categorization schemes for neuro-symbolic approaches have been recently presented (Medsker 1994 , Hilario 1997 , McGarry et al. 1999 ). Due to the richness and the variety of integration methods, not all hybrid approaches can be fully accommodated by existing categorization schemes. Such a case involves certain hybrid approaches that offer a unified neuro-symbolic knowledge representation scheme, which provides the basic functions of expert systems. This paper focuses on these approaches. Two categories of such approaches are distinguished: one giving pre-eminence to connectionist and one giving pre-eminence to the symbolic framework. The systems of the second category are proven to be more advantageous than the systems of the first category, as far as expert systems functionalities are concerned. Those two categories constitute a new more general category of integrated systems, called 'representational integrations'. This paper is organized as follows. Section 2 discusses background knowledge focusing on the advantages and disadvantages of symbolic rules and neural networks. In Section 3, a critical overview of existing categorization schemes is made. Section 4 presents rule-based neuro-symbolic integrations for knowledge representation in expert systems, which do not exactly fit into the existing categories, and introduces a new category. Finally, section 5 concludes the paper.
doi:10.3233/his-2004-13-401 fatcat:uzemw427gjg6vj4tjqy6biddim