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Using Knowledge Engineering for Planning Techniques to leverage the BPM life-cycle for dynamic and adaptive processes

The approach here presented deals with the development of AI P&S Knowledge Engineering techniques in order to: (1) automatically generate planning domains from expert knowledge described in BPM (Muehlen and Ho 2006) process models,(2) automatically generate plans, upon these domains, that can be interpreted as business process, and (3) automatically transform these plans back into executable business processes. The interest of this work is focused on business processes the deployment and execution of which strongly depends on the given context of an organization and, therefore, do not respond to a fixed pattern. An example of such processes may be the organizational process to manage the collaborative creation of e-learning courses within a virtual learning center (a special case of product development processes). Upon a customer request, the manager of the organization needs an estimation of the tasks to be accomplished, the resources of the organization to be used in the elaboration of the course, as well as the time needed to deploy the requested course. Under these circumstances, since the final workflow to be carried out cannot be easily devised a priori, managers and decision makers rely on either project management or business process simulation tools to support decisions about activity planning (in order to find adequate dependencies between tasks and their time and resources constraints), by determining various scenarios and simulate them. But these tools force a knowledge worker to carry out a trial-and-error process that may become unrealistic when either the number of alternatives courses of action makes unmanageable to ascertain which tasks should be considered or the constraints get harder. Therefore, it is widely recognized (WfMC 2010) that, at this step, the life-cycle of BPM presents some weak points and new techniques must be developed at the process modeling/generation step, in order to fully cover the needs of knowledge workers for dynamic, adaptable processes. From the AI P&S point of view, the need to obtain a context dependent, concrete workflow from a given business process model can be seen as the problem of obtaining a situated plan from an original process model. That is, a plan