Knowledge elicitation via extension of fragmental knowledge pieces

The paper describes an advanced methodology of automatic knowledge elicitation. It merges fragmental uncertain knowledge pieces into the prior distribution of unknown parameter of a probabilistic model of a dynamic system. Careful knowledge elicitation helps in achieving as bump-less start of model-based controllers as possible. It is also important when observed data are poorly informative, which is a typical situation in closed control loops. Rigorous use of the Bayesian paradigm to the knowledge elicitation forms the essence of the methodology. Unlike former solutions, it can handle fragmental and incompletely compatible knowledge pieces in a systematic way. The description of the methodology and of the uniform model relating knowledge pieces to the ideal merger dominate the paper. An illustrative example is presented.