Abstract
Agent-based technologies, originally proposed with the aim of assisting human activities, have been recently adopted in industry for automating business processes. Business Process Model and Notation (BPMN) is a standard notation for modeling business processes, that provides a rich graphical representation that can be used for common understanding of processes but also for automation purposes. We propose a normal form of Business Process Diagrams based on Activity Theory that can be transformed to a Causal Bayesian Network, which in turn can be used to model the behavior of activity participants and assess human decision through user agents. We illustrate our approach on an Elderly health care scenario obtained from an actual contextual study.
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References
Bobek, S., Baran, M., Kluza, K., Nalepa, G.J.: Application of bayesian networks to recommendations in business process modeling. In: Proceedings of the Workshop AI Meets Business Processes 2013, CEUR, vol. 1101, pp. 41–50 (2013)
Ceballos, H., García-Vázquez, J.P., Brena, R.: Using activity theory and causal diagrams for designing multiagent systems that assist human activities. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013, Part I. LNCS, vol. 8265, pp. 185–198. Springer, Heidelberg (2013)
Endert, H., Kuster, T., Hirsch, B., Albayrak, S.: Mapping BPMN to agents: An analysis. Agent, Web Services, and Ontologies Integrated Methodologies. In: International Workshop MALLOW-AWESOME 2007, pp. 43–58 (2007)
Engeström, Y., Miettinen, R., Punamäki, R.: Perspectives on Activity Theory. Learning in Doing: Social, Cognitive and Computational Perspectives, Cambridge University Press, New York (1999)
Garcia-Vazquez, J.P., Rodriguez, M.D., Tentori, M.E., Saldana, D., Andrade, A.G., Espinoza, A.N.: An agent-based architecture for developing activity-aware systems for assisting elderly. J. Univ. Comput. Sci. 16(12), 1500–1520 (2010)
Herbert, L., Sharp, R.: Precise quantitative analysis of probabilistic business process model and notation workflows. J. Comput. Inf. Sci. Eng. 13(1), 011007(1–9) (2013)
Hinge, K., Ghosey, A., Koliadisz, G.: Process seer: A tool for semantic effect annotation of business process models. In: Proceedings of 13th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2009, pp. 54–63 (2009)
Horrocks, I., Patel-Schneider, P., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A semantic web rule language combining OWL and RuleML. W3C Member Submission, 21 May 2004
Jander, K., Braubach, L., Pokhar, A., Lamersdorf, W., Wack, K.J.: Goal-oriented processes with GPMN. Int. J. Artif. Intel. Tools 20, 1021–1041 (2011)
Kuster, T., Lutzenberger, M., Hessler, A., Hirsh, B.: Integrating process modelling into multi-agent systems engineering. In: Multiagent and Grid Systems, pp. 105–124 (2012)
Leont’ev, A.: Activity, Consciousness, and Personality. Prentice-Hall, Englewood Cliffs (1978)
zur Muehlen, M., Indulska, M.: Modeling languages for business processes and business rules: A representational analysis. Inf. Syst. 35(4), 379–390 (2010)
OMG: Business Process Model and Notation (BPMN), Version 2.0, January 2011. http://www.omg.org/spec/BPMN/2.0
Ouyang, C., van der Aalst, W., Dumas, M., Hofstede, A.: Translating BPMN to BPEL. BPM Center Report BPM-06-02, BPMcenter.org (2006)
Pearl, J.: Causality. Models, Reasoning, and Inference. Cambridge University Press, New York (2000)
Pearl, J., Robins, J.: Probabilistic evaluation of sequential plans for causal models with hidden variables. In: Besnard, P., Hanks, S. (eds.) Uncertainty in Artificial Intelligence, vol. 11, pp. 444–453 (1995)
Prandi, D., Quaglia, P., Zannone, N.: Formal analysis of BPMN via a translation into COWS. In: Lea, D., Zavattaro, G. (eds.) COORDINATION 2008. LNCS, vol. 5052, pp. 249–263. Springer, Heidelberg (2008)
Acknowledgments
This research was supported by Tecnologico de Monterrey through the “Intelligent Systems” research group, and by CONACyT through the grant CB-2011-01-167460.
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Ceballos, H.G., Flores-Solorio, V., Garcia, J.P. (2015). A Probabilistic BPMN Normal Form to Model and Advise Human Activities. In: Baldoni, M., Baresi, L., Dastani, M. (eds) Engineering Multi-Agent Systems. EMAS 2015. Lecture Notes in Computer Science(), vol 9318. Springer, Cham. https://doi.org/10.1007/978-3-319-26184-3_4
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DOI: https://doi.org/10.1007/978-3-319-26184-3_4
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