COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS

Abstract

The subject of the research is the processes of constructing explanations based on causal relationships between states or actions of an intellectual
system. An explanation is knowledge about the sequence of causes and effects that determine the process and result of an intelligent information
system. The aim of the work is to develop a counterfactual temporal model of cause-and-effect relationships as part of an explanation of the process of
functioning of an intelligent system in order to ensure the identification of causal dependencies based on the analysis of the logs of the behavior of
such a system. To achieve the stated goals, the following tasks are solved: determination of the temporal properties of the counterfactual description of
cause-and-effect relationships between actions or states of an intelligent information system; development of a temporal model of causal connections,
taking into account both the facts of occurrence of events in the intellectual system, and the possibility of occurrence of events that do not affect the
formation of the current decision. Conclusions. The structuring of the temporal properties of causal links for pairs of events that occur sequentially in
time or have intermediate events is performed. Such relationships are represented by alternative causal relationships using the temporal operators
"Next" and "Future", which allows realizing a counterfactual approach to the representation of causality. A counterfactual temporal model of causal
relationships is proposed, which determines deterministic causal relationships for pairs of consecutive events and pairs of events between which there
are other events, which determines the transitivity property of such dependencies and, accordingly, creates conditions for describing the sequence of
causes and effects as part of the explanation in intelligent system with a given degree of detail The model provides the ability to determine cause-andeffect relationships, between which there are intermediate events that do not affect the final result of the intelligent information system.