Handling Uncertainty in Rules

  • Grzegorz J. NalepaEmail author
Part of the Intelligent Systems Reference Library book series (ISRL, volume 130)


In this chapter we present extensions to the XTT model aimed at handling uncertain knowledge. The primary motivation for this research were studies in the area of the context-aware systems. We implemented such systems on mobile platforms, including smartphones or tablets. Such an environment poses a number of challenges addressed by our work. In this chapter we present the classification of most common uncertainty sources present in mobile context-aware systems. We provide a short survey of methods that aim at modeling and handling these uncertainties. We present the approach developed for XTT to cover uncertainties caused by the imprecise data based on modified certainty factors algebra. Furthermore, we discuss its probabilistic extensions. Then the time-parametrised operators for handling noisy batches of data are provided. Finally, we give an insight into a probabilistic interpretation of rule-based models for handling uncertainties caused by the missing data.


  1. 1.
    Bobek, S.: Methods for modeling self-adaptive mobile context-aware systems. Ph.D. thesis, AGH University of Science and Technology (April 2016) Supervisor: Grzegorz J. NalepaGoogle Scholar
  2. 2.
    Bobek, S., Nalepa, G.J.: Uncertain context data management in dynamic mobile environments. Future Gener. Comput. Syst. 66, 110–124 (2017)CrossRefGoogle Scholar
  3. 3.
    Bobek, S., Nalepa, G.J.: Uncertainty handling in rule-based mobile context-aware systems. Pervasive and Mobile Computing (2016)Google Scholar
  4. 4.
    Kjaer, K.E.: A survey of context-aware middleware. In: Proceedings of the 25th Conference on IASTED International Multi-Conference: Software Engineering, SE’07, pp. 148–155. ACTA Press (2007)Google Scholar
  5. 5.
    Benerecetti, M., Bouquet, P., Bonifacio, M.: Distributed context-aware systems. Hum. Comput. Interact. 16(2), 213–228 (2001)CrossRefGoogle Scholar
  6. 6.
    Hu, H., of Hong Kong, U.: ContextTorrent: A Context Provisioning Framework for Pervasive Applications. University of Hong Kong (2011)Google Scholar
  7. 7.
    Chen, H., Finin, T.W., Joshi, A.: Semantic web in the context broker architecture. In: PerCom, IEEE Computer Society, pp. 277–286 (2004)Google Scholar
  8. 8.
    Nalepa, G.J., Bobek, S.: Rule-based solution for context-aware reasoning on mobile devices. Comput. Sci. Inf. Syst. 11(1), 171–193 (2014)CrossRefGoogle Scholar
  9. 9.
    Parsons, S., Hunter, A.: A review of uncertainty handling formalisms. In: Hunter, A., Parsons, S. (eds.) Applications of Uncertainty Formalisms. Lecture Notes in Computer Science, vol. 1455, pp. 8–37. Springer, Berlin (1998)CrossRefGoogle Scholar
  10. 10.
    van Kasteren, T., Kröse, B.: Bayesian activity recognition in residence for elders. In: 3rd IET International Conference on Intelligent Environments, IE 07, pp. 209–212 (2007)Google Scholar
  11. 11.
    Bui, H.H., Venkatesh, S., West, G.: Tracking and surveillance in wide-area spatial environments using the abstract hidden Markov model. Int. J. Pattern Recognit. Artif. Intell. 15 (2001)Google Scholar
  12. 12.
    Fenza, G., Furno, D., Loia, V.: Hybrid approach for context-aware service discovery in healthcare domain. J. Comput. Syst. Sci. 78(4), 1232–1247 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yuan, B., Herbert, J.: Fuzzy cara - a fuzzy-based context reasoning system for pervasive healthcare. Procedia Comput. Sci. 10, 357–365 (2012)CrossRefGoogle Scholar
  14. 14.
    Hao, Q., Lu, T.: Context modeling and reasoning based on certainty factor. In: PACIIA 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications, November 2009, vol. 2, pp. 38–41 (2009)Google Scholar
  15. 15.
    Almeida, A., Lopez-de Ipina, D.: Assessing ambiguity of context data in intelligent environments: towards a more reliable context managing systems. Sensors 12(4), 4934–4951 (2012)CrossRefGoogle Scholar
  16. 16.
    Krause, A., Smailagic, A., Siewiorek, D.P.: Context-aware mobile computing: learning context-dependent personal preferences from a wearable sensor array. IEEE Trans. Mob. Comput. 5(2), 113–127 (2006)CrossRefGoogle Scholar
  17. 17.
    Senge, R., Bösner, S., Dembczyński, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., Hüllermeier, E.: Reliable classification: learning classifiers that distinguish aleatoric and epistemic uncertainty. Inf. Sci. 255, 16–29 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Niederliński, A.: RMES, Rule- and Model-Based Expert Systems. Jacek Skalmierski Computer Studio (2008)Google Scholar
  19. 19.
    Köping, L., Grzegorzek, M., Deinzer, F., Bobek, S., Ślażyński, M., Nalepa, G.J.: Improving indoor localization by user feedback. In: 2015 18th International Conference on Information Fusion (Fusion), July 2015, pp. 1053–1060 (2015)Google Scholar
  20. 20.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  21. 21.
    Korver, M., Lucas, P.J.F.: Converting a rule-based expert system into a belief network. Med. Inform. 18, 219–241 (1993)CrossRefGoogle Scholar
  22. 22.
    De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: a probabilistic prolog and its application in link discovery. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI’07, San Francisco, CA, USA, pp. 2468–2473. Morgan Kaufmann Publishers Inc. (2007)Google Scholar
  23. 23.
    Poole, D., Mackworth, A.K.: Artificial Intelligence – Foundations of Computational Agents. Cambridge University Press, Cambridge (2010)CrossRefzbMATHGoogle Scholar
  24. 24.
    Kang, D., Sohn, J., Kwon, K., Joo, B.G., Chung, I.J.: An intelligent dynamic context-aware system using fuzzy semantic language. In: Park, J.J., Adeli, H., Park, N., Woungang, I. (eds.) MUSIC. Lecture Notes in Electrical Engineering, vol. 274, pp. 143–149. Springer, Berlin (2013)Google Scholar
  25. 25.
    Orchard, R.A.: FuzzyCLIPS Version 6.04A. User’s Guide, Integrated Reasoning Institute for Information Technology National Research Council Canada (October 1998)Google Scholar
  26. 26.
    Giarratano, J.C.: CLIPS User’s Guide (December 2007)Google Scholar
  27. 27.
    Khan, W.Z., Xiang, Y., Aalsalem, M.Y., Arshad, Q.: Mobile phone sensing systems: a survey. IEEE Commun. Surv. Tut. 15(1), 402–427 (2013)CrossRefGoogle Scholar
  28. 28.
    Heckerman, D.: Probabilistic interpretations for MYCIN’s certainty factors. In: Proceedings of the First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-85), Corvallis, Oregon, pp. 9–20. AUAI Press (1985)Google Scholar
  29. 29.
    Salber, D., Dey, A.K., Abowd, G.D.: The context toolkit: Aiding the development of context-enabled applications. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’99, New York, NY, USA, pp. 434–441. ACM (1999)Google Scholar
  30. 30.
    Etter, R., Costa, P.D., Broens, T.: A rule-based approach towards context-aware user notification services. In: 2006 ACS/IEEE International Conference on Pervasive Services, June 2006, pp. 281–284 (2006)Google Scholar
  31. 31.
    Vanrompay, Y., Kirsch-Pinheiro, M., Berbers, Y.: Context-aware service selection with uncertain context information. ECEASST 19 (2009)Google Scholar
  32. 32.
    Floch, J., Fra, C., Fricke, R., Geihs, K., Wagner, M., Lorenzo, J., Soladana, E., Mehlhase, S., Paspallis, N., Rahnama, H., Ruiz, P.A., Scholz, U.: Playing music – building context-aware and self-adaptive mobile applications. Softw. Pract. Exp. 43(3), 359–388 (2013)CrossRefGoogle Scholar
  33. 33.
    Parsaye, K., Chignell, M.: Expert Systems for Experts / Kamran Parsaye, Mark Chignell. Wiley, New York (1988)Google Scholar
  34. 34.
    Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Addison-Wesley Longman Publishing Co., Inc., Boston (1984)Google Scholar
  35. 35.
    Nalepa, G., Bobek, S., Ligęza, A., Kaczor, K.: HalVA – rule analysis framework for XTT2 rules. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) Rule-Based Reasoning, Programming, and Applications. Lecture Notes in Computer Science, vol. 6826, pp. 337–344. Springer, Berlin (2011)CrossRefGoogle Scholar
  36. 36.
    Bobek, S., Nalepa, G.: Compact representation of conditional probability for rule-based mobile context-aware systems. In: Bikakis, A., Fodor, P., Roman, D. (eds.) Rules on the Web: From Theory to Applications. Lecture Notes in Computer Science. Springer International Publishing, Berlin (2015)Google Scholar
  37. 37.
    Nalepa, G., Bobek, S., Ligęza, A., Kaczor, K.: Algorithms for rule inference in modularized rule bases. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) Rule-Based Reasoning, Programming, and Applications. Lecture Notes in Computer Science, vol. 6826, pp. 305–312. Springer, Berlin (2011)CrossRefGoogle Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland

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