What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

GameOfFlows: Process Instance Adaptation in Complex, Dynamic and Potentially Adversarial Domains

Business processes often need to be executed in complex settings where a range of environmental factors can conspire to impede the execution of the process. Gou et al. [1] view process execution as an adversarial game between the process player and the environment player. While useful, their approach leaves open the question of the role of the original process design in the story. Process designs encode significant specialist knowledge and have significant investments in process infrastructure associated with them. We provide a machinery that involves careful deliberation on when and where to deviate from a process design. We conceive of a process engine that frequently (typically after executing each task) re-considers the next task or sequence of tasks to execute. It performs trade-off analysis by comparing the following: (1) the likelihood of successful completion by conforming to the mandated process design against (2) the likelihood of success if it were to deviate from the design by executing a compensation (i.e., an alternative sequence of tasks that takes the process from the current state to completion)