Distributed dynamic decision-making and learning under uncertainty in complex and changing situations are emerging as the key competencies required to support future information-based systems. The Bayesian paradigm is acknowledged to provide a consistent and rigorous theoretical basis for joint learning and dynamic decision-making. The established theory already provides a class of efficient adaptive strategies. However, this approach fails to overcome the computational complexity barrier encountered in complex settings.
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.