Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942326 | Cognitive Systems Research | 2018 | 26 Pages |
Abstract
The integration between connectionist learning and logic-based reasoning is a longstanding foundational question in artificial intelligence, cognitive systems, and computer science in general. Research into neural-symbolic integration aims to tackle this challenge, developing approaches bridging the gap between sub-symbolic and symbolic representation and computation. In this line of work the core method has been suggested as a way of translating logic programs into a multilayer perceptron computing least models of the programs. In particular, a variant of the core method for three valued Åukasiewicz logic has proven to be applicable to cognitive modelling among others in the context of Byrne's suppression task. Building on the underlying formal results and the corresponding computational framework, the present article provides a modified core method suitable for the supervised learning of Åukasiewicz logic (and of a closely-related variant thereof), implements and executes the corresponding supervised learning with the backpropagation algorithm and, finally, constructs a rule extraction method in order to close the neural-symbolic cycle. The resulting system is then evaluated in several empirical test cases, and recommendations for future developments are derived.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Frederik Harder, Tarek R. Besold,