Abstract
Understanding what the user is doing in a Smart Environment is important not only for adapting the environment behavior, e.g. by providing the most appropriate combination of services for the recognized situation, but also for identifying situations that could be problematic for the user. Manually building models of the user processes is a complex, costly and error-prone engineering task. Hence, the interest in automatically learning them from examples of actual procedures. Incremental adaptation of the models, and the ability to express/learn complex conditions on the involved tasks, are also desirable. First-order logic provides a single comprehensive and powerful framework for supporting all of the above. This paper presents a First-Order Logic incremental method for inferring process models, and show its application to the user’s daily routines, for predicting his needs and comparing the actual situation with the expected one. Promising results have been obtained with both controlled experiments that proved its efficiency and effectiveness, and with a domain-specific dataset.
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References
Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)
Bierhoff, I., van Berlo, A.: More intelligent smart houses for better care and health, global telemedicine and ehealth updates. Knowledge Resources 1, 322–325 (2008)
Bombini, G., Di Mauro, N., Esposito, F., Ferilli, S.: Incremental learning from positive examples. In: Atti del 24-esimo Convegno Italiano di Logica Computazionale, CILC 2009 (2009)
Cattafi, M., Lamma, E., Riguzzi, F., Storari, S.: Incremental declarative process mining. In: Szczerbicki, E., Nguyen, N.T. (eds.) Smart Information and Knowledge Management. SCI, vol. 260, pp. 103–127. Springer, Heidelberg (2010)
Cook, D., Schmitter-Edgecombe, M.: Assessing the quality of activities in a smart environment. Methods of Information in Medicine (2009)
Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. Technical Report CU-CS-819-96, Department of Computer Science, University of Colorado (1996)
Cook, J.E., Wolf, A.L.: Event-based detection of concurrency. Technical Report CU-CS-860-98, Department of Computer Science, University of Colorado (1998)
de Medeiros, A.K.A., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic process mining: an experimental evaluation. Data Min. Knowl. Discov. 14, 245–304 (2007)
Ellis, C.A., Keddara, K., Rozenberg, G.: Dynamic change within workflow systems. In: Proceedings of the Conference on Organizational Computing Systems, pp. 10–21. ACM (1995)
Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy theory revision: Induction and abduction in inthelex. Machine Learning Journal 38(1/2), 133–156 (2000)
Herbst, J.: Inducing workflow models from workflow instances. In: Proceedings of the 6th European Concurrent Engineering Conference, pp. 175–182. Society for Computer Simulation, SCS (1999)
Herbst, J.: Dealing with concurrency in workflow induction. In: Proceedings of the European Concurrent Engineering Conference, pp. 175–182. SCS Europe (2000)
Herbst, J., Karagiannis, D.: Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. In: Proceedings of the 9th International Workshop on Database and Expert Systems Applications, pp. 745–752. IEEE (1998)
Herbst, J., Karagiannis, D.: An inductive approach to the acquisition and adaptation of workflow models. In: Proceedings of the IJCAI 1999 Workshop on Intelligent Workflow and Process Management: The New Frontier for AI in Business, pp. 52–57 (1999)
Maurer, U., Smailagic, A., Siewiorek, D., Deisher, M.: Activity recognition and monitoring using multiple sensors on different body positions. In: International Workshop on Wearable and Implantable Body Sensor Networks, pp. 4–116 (2006)
Muggleton, S.: Inductive logic programming. New Generation Computing 8(4), 295–318 (1991)
Rashidi, P., Cook, D.: Keeping the resident in the loop: Adapting the smart home to the user. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans (2012)
Steg, H., et al.: Ambient assisted living – european overview report. Technical report (September 2005)
Suchman, L.A.: Plans and situated actions: The problem of human-machine communications. Cambridge University Press (1987)
van der Aalst, W.M.P.: The application of petri nets to workflow management. The Journal of Circuits, Systems and Computers 8, 21–66 (1998)
van Kasteren, T., Krose, B.: Bayesian activity recognition in residence for elders. In: 3rd IET International Conference on Intelligent Environments (IE 2007), pp. 209–212 (2007)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data. In: Hoste, V., De Pauw, G. (eds.) Proceedings of the 11th Dutch-Belgian Conference of Machine Learning (Benelearn 2001), pp. 93–100 (2001)
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Ferilli, S., De Carolis, B., Redavid, D. (2013). Logic-Based Incremental Process Mining in Smart Environments. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_40
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DOI: https://doi.org/10.1007/978-3-642-38577-3_40
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