Knowledge extraction maps extensive data sets on lower dimensional objects. Its results always serve to a subsequent, often dynamic, decision making. Decision-making quality is substantially influenced by the mapping used. This simple fact is relatively rarely respected by many elements in the overwhelming arsenal of existing mappings. A complete solution of decision making problems that includes explicitly the discussed mapping are severely limited by computational complexity (labelled as curse of dimensionality). The project contributes to an improvement of this state via
i) solving general dynamic-decision tasks within a specific Bayesian methodology that uses probabilistic tools both for describing the object and strategies of decision making but also its aims and constraints;
ii) developing methodology approximating the optimal solution obtained;
iii) verifying the developed methodological and algorithmic tools on non-trivial, practically significant, decision-making problems in medicine (diagnostics of secondary lymphedema (concluded)) and economy (trading with futures).