Cognitive Business Process Management for Adaptive Cyber-Physical Processes
In the era of Big Data and Internet-of-Things (IoT), all real-world environments are gradually becoming cyber-physical (e.g., emergency management, healthcare, smart manufacturing, etc.), with the presence of connected devices and embedded ICT systems (e.g., smartphones, sensors, actuators) producing huge amounts of data and events that influence the enactment of the Cyber Physical Processes (CPPs) enacted in such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention at run-time. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS that combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on well-established action-based formalisms in Artificial Intelligence, which allow to interpret the ever-changing knowledge of cyber-physical environments and to adapt CPPs by preserving their base structure.
KeywordsCognitive business process management Cyber-Physical Processes Process adaptation and recovery Situation calculus IndiGolog Automated planning
This work is partly supported by the projects Social Museum and Smart Tourism (CTN01_00034_23154), NEPTIS (PON03PE_00214_3), RoMA (SCN_00064), and by the Sapienza project “Data-aware Adaptation of Knowledge-intensive Processes in Cyber-Physical Domains through Action-based Languages”.
- 1.Cossu, F., Marrella, A., Mecella, M., Russo, A., Bertazzoni, G., Suppa, M., Grasso, F.: Improving operational support in hospital wards through vocal interfaces and process-awareness. In: 25th International Symposium on Computer-Based Medical Systems (CBMS). IEEE (2012)Google Scholar
- 6.Marrella, A., Mecella, M., Sardiña, S.: Supporting adaptiveness of cyber-physical processes through action-based formalisms. AI Communications (2017, to appear)Google Scholar
- 7.Marrella, A., Mecella, M., Sardina, S.: SmartPM: an adaptive process management system through situation calculus, indigolog, and classical planning. In: Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning, KR 2014 (2014)Google Scholar
- 10.De Giacomo, G., Lespérance, Y., Levesque, H., Sardina, S.: IndiGolog: a high-level programming language for embedded reasoning agents. In: El Fallah Seghrouchni, A., Dix, J., Dastani, M., Bordini, R. (eds.) Multi-Agent Programming: Languages, Tools and Applications. Tools and Applications, Springer, Boston (2009). https://doi.org/10.1007/978-0-387-89299-3_2 Google Scholar
- 13.Reichgelt, H.: Knowledge Representation: An AI perspective. Greenwood Publishing Group Inc., Westport (1991)Google Scholar
- 15.Rajkumar, R.R., Lee, I., Sha, L., Stankovic, J.: Cyber-physical systems: the next computing revolution. In: Proceedings of the 47th Design Automation Conference, DAC 2010, pp. 731–736. IEEE (2010)Google Scholar
- 16.Lee, E.A.: Cyber physical systems: design challenges. In: Proceedings of the 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing, ISORC 2008, pp. 363–369. IEEE (2008)Google Scholar
- 17.De Giacomo, G., Reiter, R., Soutchanski, M.: Execution monitoring of high-level robot programs. In: Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning, KR 1998, pp. 453–465 (1998)Google Scholar
- 18.Gerevini, A., Saetti, A., Serina, I., Toninelli, P.: LPG-TD: a fully automated planner for PDDL2.2 domains. In: Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004 (2004)Google Scholar