Fuzzy Conformance Checking of Observed Behaviour with Expectations

  • Stefano Bragaglia
  • Federico Chesani
  • Paola Mello
  • Marco Montali
  • Davide Sottara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6934)


In some different research fields a research issue has been to establish if the external, observed behaviour of an entity is conformant to some rules/specifications/expectations. Research areas like Multi Agent Systems, Business Process, and Legal/Normative systems, have proposed different characterizations of the same problem, named as the conformance problem. Most of the available systems, however, provide only simple yes/no answers to the conformance issue.

In this paper we introduce the idea of a gradual conformance, expressed in fuzzy terms. To this end, we present a system based on a fuzzy extension of Drools, and exploit it to perform conformance tests. In particular, we consider two aspects: the first related to fuzzy ontological aspects, and the second about fuzzy time-related aspects. Moreover, we discuss how to conjugate the fuzzy contributions from these aspects to get a single, fuzzy score representing a conformance degree.


fuzzy conformance production rule systems expectations time reasoning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alberti, M., Chesani, F., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: Verifiable agent interaction in abductive logic programming: The SCIFF framework. ACM Trans. Comput. Logic 9(4), 1–43 (2008)CrossRefGoogle Scholar
  2. 2.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bobillo, F., Straccia, U.: fuzzydl: An expressive fuzzy description logic reasoner. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2008 (IEEE World Congress on Computational Intelligence), pp. 923–930 (2008)Google Scholar
  4. 4.
    Bobillo, F., Straccia, U.: Fuzzy ontology representation using owl 2. International Journal of Approximate Reasoning (to appear, 2011)Google Scholar
  5. 5.
    Bragaglia, S., Chesani, F., Mello, P., Sottara, D.: A rule-based implementation of fuzzy tableau reasoning. In: Dean, et al. (eds.) [8], pp. 35–49Google Scholar
  6. 6.
    Crespo, F., de la Encina, A., Llana, L.: Fuzzy-timed automata. In: Hatcliff, J., Zucca, E. (eds.) FMOODS 2010. LNCS, vol. 6117, pp. 140–154. Springer, Heidelberg (2010), doi:10.1007/978-3-642-13464-712CrossRefGoogle Scholar
  7. 7.
    De Capua, C., De Falco, S., Liccardo, A., Morello, R.: A technique based on uncertainty analysis to qualify the design of measurement systems. In: Proceedings of the 2005 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, pp. 97–102 (May 2005)Google Scholar
  8. 8.
    Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.): RuleML 2010. LNCS, vol. 6403. Springer, Heidelberg (2010)Google Scholar
  9. 9.
    Desai, N., Chopra, A.K., Singh, M.P.: Representing and reasoning about commitments in business processes. In: AAAI, pp. 1328–1333. AAAI Press, Menlo Park (2007)Google Scholar
  10. 10.
    Dubois, D., Prade, H.: Tolerant fuzzy pattern matching: An introduction. In: Bosc, P., Kacprzyk, J. (eds.) Fuzziness in Database Management Systems, pp. 42–58. Physica-Verlag, Heidelberg (1995)CrossRefGoogle Scholar
  11. 11.
    Dubois, D., Prade, H., Testemale, C.: Weighted fuzzy pattern matching. Fuzzy Sets Syst. 28, 313–331 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Forgy, C.: Rete: A fast algorithm for the many patterns/many objects match problem. Artif. Intell. 19(1), 17–37 (1982)CrossRefGoogle Scholar
  13. 13.
    Fornara, N., Colombetti, M.: A commitment-based approach to agent communication. Applied Artificial Intelligence 18(9-10), 853–866 (2004)CrossRefGoogle Scholar
  14. 14.
    Governatori, G., Rotolo, A.: Norm compliance in business process modeling. In: Dean, et al. (eds.) [8], pp. 194–209Google Scholar
  15. 15.
    HongKang, Z., XueLi, Y., GuangPing, Z., Kun, H.: Research on services matching and ranking based on fuzzy qos ontology. In: 2010 International Conference on Computational Aspects of Social Networks (CASoN), pp. 579–582 (September 2010)Google Scholar
  16. 16.
    JBossL. JBoss Drools 5.0 - Business Logic Integration Platform (2010)Google Scholar
  17. 17.
    Lee, C.-H.L., Liu, A., Hung, J.-S.: Service quality evaluation by personal ontology. J. Inf. Sci. Eng. 25(5), 1305–1319 (2009)Google Scholar
  18. 18.
    Montali, M., Pesic, M., Aalst, W.M.P.v.d., Chesani, F., Mello, P., Storari, S.: Declarative specification and verification of service choreographiess. TWEB 4(1) (2010)Google Scholar
  19. 19.
    Ragone, A., Straccia, U., Noia, T.D., Sciascio, E.D., Donini, F.M.: Fuzzy matchmaking in e-marketplaces of peer entities using datalog. Fuzzy Sets Syst. 160, 251–268 (2009)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Rosario, S., Benveniste, A., Haar, S., Jard, C.: Probabilistic qos and soft contracts for transaction-based web services orchestrations. IEEE Trans. Serv. Comput. 1, 187–200 (2008)CrossRefGoogle Scholar
  21. 21.
    Singh, M.P., Chopra, A.K., Desai, N.: Commitment-based service-oriented architecture. IEEE Computer 42(11), 72–79 (2009)CrossRefGoogle Scholar
  22. 22.
    Sora, I., Lazar, G., Lung, S.: Mapping a fuzzy logic approach for qos-aware service selection on current web service standards. In: 2010 International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), pp. 553–558 (May 2010)Google Scholar
  23. 23.
    Sottara, D., Mello, P., Proctor, M.: A configurable rete-oo engine for reasoning with different types of imperfect information. IEEE Trans. Knowl. Data Eng. 22(11), 1535–1548 (2010)CrossRefGoogle Scholar
  24. 24.
    Torroni, P., Chesani, F., Mello, P., Montali, M.: Social commitments in time: Satisfied or compensated. In: Baldoni, M., Bentahar, J., van Riemsdijk, M.B., Lloyd, J. (eds.) DALT 2009. LNCS, vol. 5948, pp. 228–243. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  25. 25.
    Torroni, P., Chesani, F., Yolum, P., Gavanelli, M., Singh, M.P., Lamma, E., Alberti, M., Mello, P.: Modelling Interactions via Commitments and Expectations. In: Handbook of Research on Multi-Agent Systems: Semantics and Dynamics of Organizational Models, pp. 263–284. IGI Global (2009)Google Scholar
  26. 26.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stefano Bragaglia
    • 1
  • Federico Chesani
    • 1
  • Paola Mello
    • 1
  • Marco Montali
    • 2
  • Davide Sottara
    • 1
  1. 1.DEISUniversity of BolognaBolognaItaly
  2. 2.KRDB Research CentreFree University of Bozen-BolzanoBolzanoItaly

Personalised recommendations