In this direction, a number of techniques from the field of AI have been applied to BPMSs with the aim of increasing the degree of automated process adaptation (Marrella, 2018, 2019). In (Gajewski et al., 2005; Ferreira and Ferreira, 2006; Marrella and Lespérance, 2013, 2017), if a task failure occurs at run-time and leads to a process goal violation, a new complete process definition that complies with the goal is generated relying on a partial-order AI planner. As a side effect, this often significantly modifies the assignment of tasks to process participants. The work (Bucchiarone et al., 2011) proposes a goal-driven approach to adapt processes to run-time context changes. Process and context changes that prevent goal achievement are specified at design-time and recovery strategies are built at run-time through an adaptation mechanism based on service composition via AI planning.