Uncertainty modelling in multi-agent information fusion systems

In the field of informed decision-making, the usage of a single diagnostic expert system has limitations when dealing with complex circumstances. The usage of a multi-agent information fusion (MAIF) system can mitigate this situation, as it allows multiple agents collaborating together to solve the problems in a complex environment. However, the MAIF system needs to handle the uncertainty problem between different agents objectively at the same time. Aiming at this goal, this study reconstructs the generation of basic probability assignments (BPAs) based on the framework of evidence theory and presents the uncertainty relationship between recognition sets, which are beneficial to the applications of the MAIF system. On the basis of evidence distance measurement, our method demonstrates the effectiveness and extendibility in numerical examples, and improves the accuracy and anti-interference ability during the identification process in the MAIF system.