Automated Discovery of Concurrent Models of Decision-Making Systems from Data

The paper presents a methodology for building concurrent models of decision-making systems based on knowledge extracted from empirical data. We assume that the data is represented by a decision table, while the decision-making system is represented by a Petri net. Decision tables contain conditional attribute values obtained from measurements or other sources. A Petri net is constructed using all true and acceptable rules generated from a given decision table. Rule factors and other parameters needed to build the net model are also computed from the data table. Three operators In, Trs and Out interpreted as uninorms are used to describe the dynamics of the net model. The expected behavior of the model is achieved by proper organization of its work. The theoretical basis of the methodology is the concepts, methods and algorithms derived from the theory of rough sets, fuzzy sets and Petri nets.