Reasoning Algorithm for the Creative Decision Support System Integrating Inference and Machine Learning

Abstract

In this paper, a reasoning algorithm for a creative decision support system is proposed. It allows for the integration of inference and machine learning algorithms. The execution of the learning algorithm is automatic since it is formalized by applying a complex inference rule, which generates intrinsically new knowledge using the facts already stored in the knowledge base as training data. This new knowledge may be used in the same inference chain to derive a decision. A solution of this type makes the reasoning process more creative and also allows for its continuation in cases when the knowledge base does not have the appropriate explicitly encoded knowledge. In this paper, an appropriate knowledge representation and inference model are proposed. Experimental verication is performed on the decision support system operating in a casting domain.