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. This project aims to create a theoretical and algorithmic basis of a mathematically rigorous, but computationally tractable Bayesian distributed dynamic decision-making system, fully scalable in the number of local decision makers.

Objectives

The project aims to develop theory, algorithms and software for Bayesian distributed dynamic decision-making. It will make a qualitatively new step towards a generic theory of multi-participant, multi-step decision making in complex dynamic situations. The project will transform the theory into a generic algorithmic and software toolset.

The theory and its conversion into a practical tool will provide: 

1.  Rigorous learning methods applicable both to quantitative and qualitative data. 
2.  Learning of dynamic, mixed-data,probabilistic mixtures. 
3.  Computationally tractable approximation to the fully probabilistic design of decision-making strategies. 
4.  Design of a tool set for tailoring of the generic decision-making tools to a specific application. 
5.  Numerically robust algorithmic counterparts of theoretical operations. 
6.  Structured training materials for users of a varying level of competence and background. 
7.  Portable software implementation allowing accommodation of new elements developed in the heterogeneous research situation.

Applications to non-trivial problems will be used to measure the project?s success. Simulation, pilot-plants and real-life (in rolling mill industry) tests will serve this purpose.

Contact:

M. Kárný