Development of process-oriented explanations in intelligent information systems based on possibilistic causal dependencies

The subject of the study is the process of constructing explanations in intelligent information systems represented as a «gray box.» The aim is to develop a process-oriented approach to forming explanations that ensures support and improvement of decision-making processes in intelligent information systems. The research objectives include the development of a process model for explanations based on possibilistic causal dependencies and the creation of a method for constructing process-oriented representations of explanations. The proposed explanation approach considers temporal and causal relationships between states in the decision-making process. The scientific novelty lies in the development of a generalized process model for explanations, which enables detailing the sequence of states in decision-making processes, identifying key actions, and contextual conditions for their execution. The model creates conditions for determining process actions with the highest possibility values, thus having the most significant impact on intelligent system decisions. The proposed method for constructing process-oriented explanations in intelligent information systems based on possibilistic causal dependencies includes stages such as forming temporal and causal rules, calculating possibility and necessity metrics, and presenting explanations considering state attributes in the decision-making process. The method is designed to support internal users of intelligent systems by providing interpretable representations of decision-making processes.