The recommendation system knowledge representation and reasoning procedures under uncertainty for metal casting

The paper presents an information system dedicated to requirements recommendation and knowledge sharing. It presents methodology of constructing domain knowledge base and application procedure on the example of production technology of Austempered Ductile Iron (ADI). For knowledge representation and reasoning Logic of Plausible Reasoning (LPR) is used. Both equally applicable LPR for formalization the knowledge of foundry technology, as well as the described system solution have the unique character

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

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