Explaining non-compliance of business process models through automated planning

Modern companies execute business processes to deliver products and services, whose enactment requires to adhere to laws and regulations. Compliance checking is the task of identifying potential violations of such requirements prior to process execution. Traditional approaches to compliance checking employ formal verification techniques (e.g., model checking) to identify which process paths in a process model may lead to violations. However, this diagnostics is, in most of the cases, not rich enough for the user to understand how the process model should be changed to solve the violations. In this paper, we present an approach based on finite-state automata manipulation to identify the specific process activities that are responsible to cause violations and, in some cases, suggest reparative actions to be applied to the process model to solve the violations. We show that our approach can be expressed as a planning problem in Artificial Intelligence, which can be efficiently solved by state-of-the-art planners. We report experimental results using synthetic case studies of increasing complexity to show the scalability of our approach.