Achieving framed autonomy in AI-augmented business process management systems through automated planning

AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems empowered by Artificial Intelligence (AI) technology for autonomously unfolding and adapting the execution flow of business processes (BPs) within a set of potentially conflicting procedural and declarative constraints, called process framing. In this respect, framed autonomy enables an ABPMS to autonomously decide how to progress the execution of a BP, as long as the boundaries imposed by the frame are respected. Among these constraints, there could be a partial BP execution that needs to be completed, activating a different near-optimal framing that enables the BP to progress its execution. In this paper, we present an automata-based technique that pairs constraint-based framing with automated planning in AI to recommend, given a partial BP execution trace, the continuation of that trace that minimizes the violation cost of the conforming space defined by the process frame. We report on the results of experiments of increasing complexity to showcase our technique’s performance and scalability.