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

Dorota Wilk-Kolodziejczyk

doi:10.7494/csci.2017.18.3.2364

Authors

Dorota Wilk-Kolodziejczyk AGH, University of Science and Technology Foundry Research Institute http://orcid.org/0000-0001-5590-0477

DOI:

https://doi.org/10.7494/csci.2017.18.3.2364

Keywords:

reasoning algorithm, inferential theory of learning, decision support, rule induction, logic of plausible reasoning

Abstract

In this paper a reasoning algorithm for a creative decision support system is proposed. It allows to integrate inference and machine learning algorithms. Execution of learning algorithm is automatic because it is formalized as aplying a complex inference rule, which generates intrinsically new knowledge using the facts stored already in the knowledge base as training data. This new knowledge may be used in the same inference chain to derive a decision. Such a solution makes the reasoning process more creative and allows to continue resoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate knowledge representation and infeence model are proposed. Experimental verification is performed on a decision support system in a casting domain.

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