Affiliations: [a] University of Patras, School of Engineering, Dept of Computer Engineering & Informatics, 26500 Patras, Hellas, Greece. ihatz@ceid.upatras.gr | [b] Research Academic Computer Technology Institute, P.O. Box 1122, 26110 Patras, Hellas, Greece. prentzas@ceid.upatras.gr
Note: [*] The order is alphabetical.
Abstract: 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 inference 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
