Quality of maintaining of complex man-machine systems very much depends on experience, skills and performance of the human decision-makers (operators) managing the system. The task is complicated by complexity and dimensionality of the system managed as well as limited abilities of the operator.
The research concerns developing prescriptive theory of Bayesian dynamic decision-making (DM) under uncertainty that allows to construct efficient adaptive DM systems and to create systems supporting human decision-makers. The adopted approach relies on black-box modeling and on the availability of informative data. Specialization of the developed theory to dynamic mixtures combined with fully probabilistic design provides a practical tool of broad applicability.
The general idea is to process historical data available to model of the managed system behavior under various working conditions in a form of multi-dimensional probability mixtures (learning phase). The mixture learned and mixture expressing DM aims are employed to build an advisory mixture describing DM strategy (design phase). Decision designed by an advisory system is the prediction of advisory mixture made for the actually incoming data. Advising supposes providing this prediction in a suitable form to the decision-maker. The decision-maker is responsible to accept or to reject the offered advise.
The established solution has proven to able to cope with dynamically changing incompletely known multi-attribute environment and to learn and optimize dynamic decision-making strategy realized either by human being or automatically.
The developed generic optimized dynamic advising covers: