Bayesian network equipped workflow engine to coordinate Artificial Intelligence for automating network operation
Ryosuke SATO, Mizuto Nakamura, Atsushi Takada, Kyoko Yamagoe · 2021
Deploying artificial intelligence (AI) to network operations have long been an issue. AI has been expected to help automating network operations, especially in those requiring human decisions, such as handling failures which involve complex decisions. Since current AIs do not have enough parameters for individual failures cases, their accuracy is not enough to fully rely on their decisions. Thus, handing unusual fault cases are still dominated by skilled operators. This paper proposes an extended Business Process Model and Notation (BPMN) which uses Bayesian networks to represent operator's decisions, to connect AIs and workflow engines (WFE) which automates handing atypical failures.