This work presents a decision-making system supported by Adaptive Coloured Petri Net (ACPN

This work presents the use of Adaptive Coloured Petri Net (ACPN) in support of decision making. ACPN is an extension of the Coloured Petri Net (CPN) that allows you to change the network topology. Usually, experts in a particular field can establish a set of rules for the proper functioning of a business or even a manufacturing process. On the other hand, it is possible that the same specialist has difficulty in incorporating this set of rules into a CPN that describes and follows the operation of the enterprise and, at the same time, adheres to the rules of good performance. To incorporate the rules of the expert into a CPN, the set of rules from the IF -THEN format to the extended adaptive decision table format is transformed into a set of rules that are dynamically incorporated to APN. The contribution of this paper is the use of ACPN to establish a method that allows the use of proven procedures in one area of knowledge (decision tables) in another area of knowledge (Petri nets and Workflows), making possible the adaptation of techniques and paving the way for new kind of analysis.

The objective of this work is the study of adaptive technologies applied to artificial neural networks and Petri nets. In addition, a methodology is proposed for these applications from the definition of an adaptive color Petri net. Initially, artificial neural networks are studied from the point of view of rule extraction. One of the recurring criticisms of artificial neural networks is the "black box" feature of the solutions, meaning that the solutions hide the working mechanism, casting doubt on the reason for its operation. The extraction of rules from the artificial neural networks aims to present an equivalent solution based on rules that for the experts in a given area is more intelligible or transparent. Another important point is the insertion of rules in artificial neural networks. This insertion is possible from the rule-based version of artificial neural networks. A human expert in an area often creates a set of rules that aid in understanding the problem. If these rules are inserted into the set of rules obtained from the data, the new set of rules will contain at the same time the human knowledge and the knowledge extracted from the data. Adaptive rule extraction and insertion technologies make solutions more flexible. Petri nets are, in a sense, complementary to artificial neural networks as they were designed to treat "Discrete Event Systems" or sequential systems, while artificial neural networks have a combinatorial nature. Many extensions have been proposed to the Petri nets over the years and among these extensions appear associations of Petri nets and artificial neural networks. In these associations, many techniques developed for artificial neural networks were incorporated into Petri nets, such as the various forms of learning. Using the Petri nets feature of sequential modeling, the training phase of artificial neural networks can be controlled by the Petri net. In this work, the incorporation of rules into the Petri net is examined as well as its application to decision support systems and flexible manufacturing systems.