Compact Representation of Conditional Probability for Rule-Based Mobile Context-Aware Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9202)


Context-aware systems gained huge popularity in recent years due to rapid evolution of personal mobile devices. Equipped with variety of sensors, such devices are sources of a lot of valuable information that allows the system to act in an intelligent way. However, the certainty and presence of this information may depend on many factors like measurement accuracy or sensor availability. Such a dynamic nature of information may cause the system not to work properly or not to work at all. To allow for robustness of the context-aware system an uncertainty handling mechanism should be provided with it. Several approaches were developed to solve uncertainty in context knowledge bases, including probabilistic reasoning, fuzzy logic, or certainty factors. In this paper, we present a representation method that combines strengths of rules based on the attributive logic and Bayesian networks. Such a combination allows efficiently encode conditional probability distribution of random variables into a reasoning structure called XTT2. This provides a method for building hybrid context-aware systems that allows for robust inference in uncertain knowledge bases.


Context-awareness Mobile devices Knowledge management Uncertainty Probabilistic rules 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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
  2. 2.
    Benerecetti, M., Bouquet, P., Bonifacio, M., Italia, A.A.: Distributed context-aware systems (2001)Google Scholar
  3. 3.
    Bobek, S., Nalepa, G.J., Ligȩza, A., Adrian, W.T., Kaczor, K.: Mobile context-based framework for threat monitoring in urban environment with social threat monitor. Multimedia Tools and Applications (2014).
  4. 4.
    Bobek, S., Nalepa, G.J.: Incomplete and uncertain data handling in context-aware rule-based systems with modified certainty factors algebra. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 157–167. Springer, Heidelberg (2014). Google Scholar
  5. 5.
    Bui, H.H., Venkatesh, S., West, G.: Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model. Intl. J. of Pattern Rec. and AI 15 (2001)Google Scholar
  6. 6.
    Chen, H., Finin, T.W., Joshi, A.: Semantic web in the context broker architecture. In: PerCom, pp. 277–286. IEEE Computer Society (2004)Google Scholar
  7. 7.
    Dey, A.K., Mankoff, J.: Designing mediation for context-aware applications. ACM Trans. Comput.-Hum. Interact. 12(1), 53–80 (2005). CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Hao, Q., Lu, T.: Context modeling and reasoning based on certainty factor. In: Asia-Pacific Conference on Computational Intelligence and Industrial Applications. PACIIA 2009, vol. 2, pp. 38–41, November 2009Google Scholar
  10. 10.
    Hu, H.: ContextTorrent: A Context Provisioning Framewrok for Pervasive Applications. University of Hong Kong (2011)Google Scholar
  11. 11.
    van Kasteren, T., Kröse, B.: Bayesian activity recognition in residence for elders. In: 3rd IET International Conference on Intelligent Environments. IE 2007, pp. 209–212 (2007)Google Scholar
  12. 12.
    Kimmig, A., Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: A short introduction to probabilistic soft logic. In: NIPS Workshop on Probabilistic Programming: Foundations and Applications (2012)Google Scholar
  13. 13.
    Kjaer, K.E.: A survey of context-aware middleware. In: Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering. SE 2007, pp. 148–155. ACTA Press (2007)Google Scholar
  14. 14.
    Kluza, K., Nalepa, G.J.: Towards rule-oriented business process model generation. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the Federated Conference on Computer Science and Information Systems - FedCSIS 2013, Krakow, Poland, September 8–11, 2013, pp. 959–966. IEEE (2013)Google Scholar
  15. 15.
    Krause, A., Smailagic, A., Siewiorek, D.P.: Context-aware mobile computing: Learning context-dependent personal preferences from a wearable sensor array. IEEE Transactions on Mobile Computing 5(2), 113–127 (2006)CrossRefGoogle Scholar
  16. 16.
    Ligȩza, A.: Logical Foundations for Rule-Based Systems. Springer-Verlag, Heidelberg (2006) Google Scholar
  17. 17.
    Ligȩza, A., Nalepa, G.J.: A study of methodological issues in design and development of rule-based systems: proposal of a new approach. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(2), 117–137 (2011)Google Scholar
  18. 18.
    Lim, B.Y., Dey, A.K.: Investigating intelligibility for uncertain context-aware applications. In: Proceedings of the 13th International Conference on Ubiquitous Computing. UbiComp 2011, pp. 415–424. ACM, New York (2011).
  19. 19.
    Lim, B.Y., Dey, A.K., Avrahami, D.: Why and why not explanations improve the intelligibility of context-aware intelligent systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI 2009, pp. 2119–2128. ACM, New York (2009).
  20. 20.
    Nalepa, G.J., Bobek, S., Ligęza, A., Kaczor, K.: Algorithms for rule inference in modularized rule bases. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 305–312. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  21. 21.
    Nalepa, G.J., Bobek, S., Ligęza, A., Kaczor, K.: HalVA - rule analysis framework for XTT2 rules. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 337–344. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  22. 22.
    Nalepa, G.J., Bobek, S.: Rule-based solution for context-aware reasoning on mobile devices. Computer Science and Information Systems 11(1), 171–193 (2014)CrossRefGoogle Scholar
  23. 23.
    Nalepa, G.J., Kluza, K., Kaczor, K.: Proposal of an inference engine architecture for business rules and processes. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 453–464. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  24. 24.
    Nalepa, G.J., Ligȩza, A., Kaczor, K.: Formalization and modeling of rules using the XTT2 method. International Journal on Artificial Intelligence Tools 20(6), 1107–1125 (2011)CrossRefGoogle Scholar
  25. 25.
    Parsons, S., Hunter, A.: A review of uncertainty handling formalisms. In: Hunter, A., Parsons, S. (eds.) Applications of Uncertainty Formalisms. LNCS (LNAI), vol. 1455, pp. 8–37. Springer, Heidelberg (1998). CrossRefGoogle Scholar
  26. 26.
    Pascalau, E., Nalepa, G.J., Kluza, K.: Towards a better understanding of the concept of context-aware business applications. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the Federated Conference on Computer Science and Information Systems - FedCSIS 2013, Krakow, Poland, September 8–11, 2013, pp. 959–966. IEEE (2013)Google Scholar
  27. 27.
    Poole, D.: The independent choice logic and beyond. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 222–243. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  28. 28.
    Poole, D., Mackworth, A.K.: Artificial Intelligence - Foundations of Computational Agents. Cambridge University Press (2010).
  29. 29.
    Raedt, L.D., Kimmig, A., Toivonen, H.: Problog: A probabilistic prolog and its application in link discovery. In: Veloso, M.M. (ed.) IJCAI, pp. 2462–2467 (2007).
  30. 30.
    Yuan, B., Herbert, J.: Fuzzy cara - a fuzzy-based context reasoning system for pervasive healthcare. Procedia CS 10, 357–365 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

Personalised recommendations