@InProceedings{2020:Venaro:BPMDS,
      AUTHOR = {Venero, Sheila and Schmerl, Bradley and Montecchi, Leonardo and Dos Reis, Julio Cesar and Rubira, Cecilia M. F.},
      TITLE = {Automated Planning for supporting Knowledge-intensive Processes},
      YEAR = {2020},
      MONTH = {8-9 June},
      BOOKTITLE = {Proceedings of the 20th Business Process Modeling, Development and Support (BPMDS) Working Conference},
      PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/Automated_Planning_for_supporting__Knowledge_intensive_Processes.pdf},
      ABSTRACT = {Knowledge-intensive processes (KiPs) are processes characterized by high levels of unpredictability and dynamism. Their process structure may not be known before their execution. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. In this paper, we consider the definition of the process structure as a planning problem. Our approach uses automated planning techniques to generate plans that define process models according to the current context. The generated plan model relies on a metamodel called METAKIP that represents the basic elements of KiPs. Our solution explores Markov Decision Processes (MDP) to generate plan models. This technique allows uncertainty representation by defining state transition probabilities, which gives us more flexibility than traditional approaches. We construct an MDP model and solve it with the help of the PRISM model-checker. The solution is evaluated by means of a proof of concept in the medical domain and reveals the feasibility of our approach.},
      KEYWORDS = {Model Checking, Stochastic Games}
}