Uncertainty Characterization and Fusion of Information from Unreliable Sources

  • Lance KaplanEmail author
  • Murat Şensoy
Part of the Information Fusion and Data Science book series (IFDS)


Intelligent systems collect information from various sources to support their decision-making. However, misleading information may lead to wrong decisions with significant losses. Therefore, it is crucial to develop mechanisms that will make such systems immune to misleading information. This chapter presents a framework to exploit reports from possibly unreliable sources to generate fused information, i.e., an estimate of the ground truth, and characterize the uncertainty of that estimate as a facet of the quality of the information. First, the basic mechanisms to estimate the reliability of the sources and appropriately fuse the information are reviewed when using personal observations of the decision-maker and known types of source behaviors. Then, we propose new mechanisms for the decision-maker to establish fused information and its quality when it does not have personal observations and knowledge about source behaviors.


Subjective logic Unreliable sources Fusion of information Quality of information Uncertainty Beliefs 



Research was sponsored by the US Army Research Laboratory and was accomplished under agreement numbers W911NF-14-1-0199. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the US Army Research Laboratory or the US government. The US government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation hereon. Dr. Şensoy thanks the US Army Research Laboratory for its support under grant W911NF-14-1-0199 and The Scientific and Technological Research Council of Turkey (TUBITAK) for its support under grant 113E238.


  1. 1.
    V. Bui, R. Verhoeven, J. Lukkien, R. Kocielnik, A trust evaluation framework for sensor readings in body area sensor networks, in Proceedings of the 8th International Conference on Body Area Networks, BodyNets ’13 (ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, 2013), pp. 495–501Google Scholar
  2. 2.
    C. Burnett, T.J. Norman, K. Sycara, Stereotypical trust and bias in dynamic multiagent systems. ACM Trans. Intell. Syst. Technol. 4(2), 26:1–26:22 (2013)Google Scholar
  3. 3.
    C. Fung, R. Boutaba, Intrusion Detection Networks: A Key to Collaborative Security (CRC Press, London, 2013)Google Scholar
  4. 4.
    S. Ganeriwal, L. Balzano, M. Srivastava, Reputation-based framework for high integrity sensor networks. ACM Trans. Sens. Netw. (ToSN) 4(3), 15 (2008)Google Scholar
  5. 5.
    S. Han, B. Koo, A. Hutter, W. Stechele, Forensic reasoning upon pre-obtained surveillance metadata using uncertain spatio-temporal rules and subjective logic, in Analysis, Retrieval and Delivery of Multimedia Content, ed. by N. Adami, A. Cavallaro, R. Leonardi, P. Migliorati (Springer, New York, 2013), pp. 125–147CrossRefGoogle Scholar
  6. 6.
    G. Han, J. Jiang, L. Shu, J. Niu, H.-C. Chao, Management and applications of trust in wireless sensor networks: a survey. J. Comput. Syst. Sci. 80(3), 602–617 (2014)CrossRefGoogle Scholar
  7. 7.
    A. Jøsang, A logic for uncertain probabilities. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 9(3), 279–311 (2001)MathSciNetCrossRefGoogle Scholar
  8. 8.
    A. Jøsang, The consensus operator for combining beliefs. Artif. Intell. J. 142(1–2), 157–170 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    A. Jøsang, Conditional reasoning with subjective logic. J. Multiple-Valued Log. Soft Comput. 15(1), 5–38 (2009)MathSciNetzbMATHGoogle Scholar
  10. 10.
    A. Jøsang, Subjective Logic: A Formalism for Reasoning Under Uncertainty (Springer, Cham, 2016)CrossRefGoogle Scholar
  11. 11.
    A. Jøsang, R. Ismail, The beta reputation system, in Proceedings of the Fifteenth Bled Electronic Commerce Conference e-Reality: Constructing the e-Economy, Bled, June 2002, pp. 48–64Google Scholar
  12. 12.
    A. Jøsang, R. Hayward, S. Pope, Trust network analysis with subjective logic, in Proceedings of the 29th Australasian Computer Science Conference, Hobart (2006), pp. 85–94Google Scholar
  13. 13.
    A. Jøsang, J. Diaz, M. Rifqi, Cumulative and averaging fusion of beliefs. Inf. Fusion 11(2), 192–200 (2010)CrossRefGoogle Scholar
  14. 14.
    A. Jøsang, T. Ažderska, S. Marsh, Trust transitivity and conditional belief reasoning, in 6th IFIP WG 11.11 International Conference, IFIPTM 2012, Surat, May 2012, pp. 68–83Google Scholar
  15. 15.
    L. Kaplan, M. Şensoy, S. Chakraborty, G. de Mel, Partial observable update for subjective logic and its application for trust estimation. Inf. Fusion 26, 66–83 (2015)CrossRefGoogle Scholar
  16. 16.
    S. Kotz, N. Balakrishnan, N.L. Johnson, Continuous Multivariate Distributions, vol. 1 (Wiley, New York, 2000)CrossRefGoogle Scholar
  17. 17.
    T.R. Levine, Encyclopedia of Deception (SAGE Publications, Los Angeles, 2014)CrossRefGoogle Scholar
  18. 18.
    Y. Liu, K. Li, Y. Jin, Y. Zhang, W. Qu, A novel reputation computation model based on subjective logic for mobile ad hoc networks. Futur. Gener. Comput. Syst. 27(5), 547–554 (2011)CrossRefGoogle Scholar
  19. 19.
    T.K. Moon, The expectation-maximization algorithm. IEEE Signal Process. Mag. 13(6), 47–60 (1996)CrossRefGoogle Scholar
  20. 20.
    T. Muller, P. Schweitzer, On beta models with trust chains, in Proceedings of Trust Management VII: 7th IFIP WG 11.11 International Conference, Malaga (2013), pp. 49–65Google Scholar
  21. 21.
    N. Oren, T.J. Norman, A. Preece, Subjective logic and arguing with evidence. Artif. Intell. 171(10), 838–854 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    J. Pasternack, D. Roth, Making better informed trust decisions with generalized fact-finding, in IJCAI (Spatial Cognition, Bremen, 2011), pp. 2324–2329Google Scholar
  23. 23.
    K. Regan, P. Poupart, R. Cohen, Bayesian reputation modeling in e-marketplaces sensitive to subjecthity, deception and change, in Proceedings of the 21st National Conference on Artificial Intelligence (AAAI Press, Menlo Park, 2006), pp. 1206–1212Google Scholar
  24. 24.
    M. Şensoy, P. Yolum, Experimental evaluation of deceptive information filtering in context-aware service selection, in International Workshop on Trust in Agent Societies (Springer, Berlin/Heidelberg, 2008), pp. 326–347Google Scholar
  25. 25.
    M. Sensoy, G. de Mel, T. Pham, L. Kaplan, T.J. Norman, TRIBE: trust revision for information based on evidence, in Proceedings of 16th International Conference on Information Fusion, Istanbul (2013)Google Scholar
  26. 26.
    M. Şensoy, L. Kaplan, G. Ayci, G. de Mel, FUSE-BEE: fusion of subjective opinions through behavior estimation, in 18th International Conference on Information Fusion, Washington, DC (2015), pp. 558–565Google Scholar
  27. 27.
    M. Şensoy, L. Kaplan, G. de Mel, T.D. Gunes, Source behavior discovery for fusion of subjective opinions, in 19th International Conference on Information Fusion, Heidelberg (2016), pp. 138–145Google Scholar
  28. 28.
    M. Şensoy, B. Yilmaz, T.J. Norman, Stage: stereotypical trust assessment through graph extraction. Comput. Intell. 32(1), 72–101 (2016)MathSciNetCrossRefGoogle Scholar
  29. 29.
    G. Shafer, A Mathematical Theory of Evidence (Princeton University Press, Princeton, 1976)zbMATHGoogle Scholar
  30. 30.
    P. Smets, The combination of evidence in the transferable belief model. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 447–458 (1990)CrossRefGoogle Scholar
  31. 31.
    W.T.L. Teacy, J. Patel, N.R. Jennings, M. Luck, TRAVOS: trust and reputation in the context of inaccurate information sources. Auton. Agents Multi-Agent Syst. 12(2), 183–189 (2006)CrossRefGoogle Scholar
  32. 32.
    W.L. Teacy, M. Luck, A. Rogers, N.R. Jennings, An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling. Artif. Intell. 193, 149–185 (2012)MathSciNetCrossRefGoogle Scholar
  33. 33.
    D. Wang, C. Huang, Confidence-aware truth estimation in social sensing applications, in 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Seattle, June 2015, pp. 336–344Google Scholar
  34. 34.
    D. Wang, L. Kaplan, T. Abdelzaher, C.C. Aggarwal, On credibility estimation tradeoffs in assured social sensing. IEEE J. Sel. Areas Commun. 31(6), 1026–1037 (2013)CrossRefGoogle Scholar
  35. 35.
    D. Wang, L. Kaplan, T.F. Abdelzaher, Maximum likelihood analysis of conflicting observations in social sensing. ACM Trans. Sens. Netw. (ToSN) 10(2), 30 (2014)Google Scholar
  36. 36.
    D. Wang, T. Abdelzaher, L. Kaplan, Social Sensing: Building Reliable Systems on Unreliable Data (Morgan Kaufmann, Waltham, 2015)CrossRefGoogle Scholar
  37. 37.
    A. Whitby, A. Jøsang, J. Indulska, Filtering out unfair ratings in Bayesian reputation systems. ICFAIN J. Manag. Res. 4(2), 48–64 (2005)Google Scholar
  38. 38.
    S. Yao, S. Hu, S. Li, Y. Zhao, L. Su, L. Kaplan, A. Yener, T. Abdelzaher, On source dependency models for reliable social sensing: algorithms and fundamental error bounds, in IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara (2016), pp. 467–476Google Scholar
  39. 39.
    X. Yin, J. Han, P.S. Yu, Truth discovery with multiple conflicting information providers on the web. IEEE Trans. Knowl. Data Eng. 20(6), 796–808 (2008)CrossRefGoogle Scholar
  40. 40.
    B. Yu, M.P. Singh, Detecting deception in reputation management, in Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (ACM, New York, 2003), pp. 73–80Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.RDRL-SES-AUS Army Research LaboratoryAdelphiUSA
  2. 2.Computer ScienceÖzyeğin UniversityIstanbulTurkey

Personalised recommendations