FzMEBN: Toward a General Formalism of Fuzzy Multi-Entity Bayesian Networks for Representing and Reasoning with Uncertain Knowledge

Riali Ishak, Fareh Messaouda, Bouarfa Hafida

2017

Abstract

Good representing and reasoning with uncertainty is a topic of growing interest within the community of artificial intelligence (AI). In this context, the Multi-Entity Bayesian Networks (MEBNs) are proposed as a candidate solution. It’s a powerful tool based on the first order logic expressiveness. Furthermore, in the last decade they have shown its effectiveness in various complex and uncertainty-rich domains. However, in most cases the random variables are vague or imprecise by nature, to deal with this problem; we have to extend the standard Multi-Entity Bayesian Networks to improve their capabilities for good representing and reasoning with uncertainty. This paper details a promising solution based on fuzzy logic; it permits to overcome the weaknesses of classical Multi-Entity Bayesian networks. In addition, we have proposed a general process for the inference task. This process contains four steps, (1) Generating a Fuzzy Situation Specific Bayesian Networks, (2) Computing fuzzy evidence, (3) Adding virtual nodes, and (4) finally, the fuzzy probabilistic inference step. Our process is based on the virtual evidence method in order to incorporate the fuzzy evidence in probabilistic inference, moreover, approximate or exact algorithms can be used, and this choice of inference type depends to the contribution of the domain expert and the complexity of the problem. Illustrative examples taken from the literatures are considered to show potential applicability of our extended MEBN.

References

  1. Cooper, G. F. (1990). The computational complexity of probabilistic inference using Bayesian belief networks. Artificial intelligence, 42(2), 393-405.
  2. Delcroix, V., Sedki, K., &Lepoutre, F. X. (2013). A Bayesian network for recurrent multi-criteria and multi-attribute decision problems: Choosing a manual wheelchair. Expert systems with applications, 40(7), 2541-2551.
  3. Fogelberg, C., Palade, V., &Assheton, P. (2008).Belief propagation in fuzzy bayesian networks. In 1st International Workshop on Combinations of Intelligent Methods and Applications (CIMA) at ECAI'08 (pp. 19-24).
  4. Golestan, K., Karray, F., &Kamel, M. S. (2013, July). High level information fusion through a fuzzy extension to multi-entity Bayesian networks in vehicular ad-hoc networks. In Information Fusion (FUSION), 2013 16th International Conference on (pp. 1180-1187).IEEE.
  5. Golestan, K., Karray, F., &Kamel, M. S. (2015, August). An integrated approach for fuzzy multi-entity bayesian networks and semantic analysis for soft and hard data fusion.In Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on (pp. 1-8).IEEE.
  6. Koller, D. and Pfeffer, A. (1997).Object-oriented Bayesian networks. In Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference, San Francisco, CA: Morgan Kaufmann.
  7. Laskey, K. B. (2008). MEBN: A language for first-order Bayesian knowledge bases. Artificial intelligence, 172(2), 140-178.
  8. Li, X. (2009, August). On the use of virtual evidence in conditional random fields. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3 (pp. 1289- 1297).Association for Computational Linguistics.
  9. Liu, Q., Tchangani, A., &Pérès, F. (2016).Modelling complex large scale systems using object oriented Bayesian networks (OOBN). IFACPapersOnLine, 49(12), 127-132.
  10. Moura, G., &Roisenberg, M. (2015, August). Probabilistic Fuzzy Bayesian Network. In Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on (pp. 476-482). IEEE.
  11. Mrad, A. B., Delcroix, V., Maalej, M. A., Piechowiak, S., &Abid, M. (2012, July). Uncertain evidence in Bayesian networks: Presentation and comparison on a simple example. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 39-48). Springer Berlin Heidelberg.
  12. Pan, H., & Liu, L. (1999). Fuzzy bayesian networks-a general formalism for representation, inference and learning with hybrid bayesian networks. In Neural Information Processing, 1999. Proceedings. ICONIP'99. 6th International Conference on (Vol. 1, pp. 401-406). IEEE.
  13. Park, C. Y., Laskey, K. B., Costa, P., & Matsumoto, S. (2013). Multi-entity bayesian networks learning in predictive situation awareness. George mason univ Fairfax va volgenau school of Information technology and engineering.
  14. Pearl J (1988) Probabilistic reasoning in intelligent systems: networksof plausible inference. Morgan Kaufmann Publishers, SanMateo.
  15. Ryhajlo, N., Sturlaugson, L., & Sheppard, J. W. (2013, September). Diagnostic Bayesian networks with fuzzy evidence. In 2013 IEEE AUTOTESTCON (pp. 1- 8).IEEE.
  16. Santos, L. L., Carvalho, R. N., Ladeira, M., &Weigang,(2016) L. A New Algorithm for Generating Situation-Specific Bayesian Networks Using BayesBall Method.
  17. Tang, H., & Liu, S. (2007, August). Basic theory of fuzzy Bayesian networks and its application in machinery fault diagnosis. In Fuzzy Systems and Knowledge Discovery, 2007.FSKD 2007. Fourth International Conference on(Vol. 4, pp. 132-137). IEEE.
  18. Waltman, L., Kaymak, U., & Van Den Berg, J. (2005). A theoretical analysis of probabilistic fuzzy systems (Doctoral dissertation, Master thesis Informatics &Economics.Erasmus University Rotterdam).
  19. Zadeh L. (1975). The Concept of a Linguistic Variable and its Application to Approximate Reasoning, International Journal of Information Science, vol. 4, n° 4, p. 301-357.
  20. Zhang, L., Wu, X., Qin, Y., Skibniewski, M. J., & Liu, W. (2016). Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel-Induced Pipeline Damage. Risk Analysis, 36(2), 278-301.
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Paper Citation


in Harvard Style

Ishak R., Messaouda F. and Hafida B. (2017). FzMEBN: Toward a General Formalism of Fuzzy Multi-Entity Bayesian Networks for Representing and Reasoning with Uncertain Knowledge . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 520-528. DOI: 10.5220/0006317205200528


in Bibtex Style

@conference{iceis17,
author={Riali Ishak and Fareh Messaouda and Bouarfa Hafida},
title={FzMEBN: Toward a General Formalism of Fuzzy Multi-Entity Bayesian Networks for Representing and Reasoning with Uncertain Knowledge},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={520-528},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006317205200528},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - FzMEBN: Toward a General Formalism of Fuzzy Multi-Entity Bayesian Networks for Representing and Reasoning with Uncertain Knowledge
SN - 978-989-758-247-9
AU - Ishak R.
AU - Messaouda F.
AU - Hafida B.
PY - 2017
SP - 520
EP - 528
DO - 10.5220/0006317205200528