Integration of Inference and Machine Learning as a Tool for Creative Reasoning

In this paper a method to integrate inference and machine learning is proposed. Execution of learning algorithm is defined as a complex inference rule, which generates intrinsically new knowledge. Such a solution makes the reasoning process more creative and allows to re-conceptualize agent's experiences depending on the context. Knowledge representation used in the model is based on the Logic of Plausible Reasoning (LPR). Three groups of knowledge transmutations are defined: search transmutations that are looking for the information in data, inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms or knowledge representation change operators. All groups can be used by inference engine in a similar manner. In the paper appropriate system model and inference algorithm are proposed. Additionally, preliminary experimental results are presented.