Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
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. 495–501
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)
C. Fung, R. Boutaba, Intrusion Detection Networks: A Key to Collaborative Security (CRC Press, London, 2013)
S. Ganeriwal, L. Balzano, M. Srivastava, Reputation-based framework for high integrity sensor networks. ACM Trans. Sens. Netw. (ToSN) 4(3), 15 (2008)
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. 125–147
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)
A. Jøsang, A logic for uncertain probabilities. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 9(3), 279–311 (2001)
A. Jøsang, The consensus operator for combining beliefs. Artif. Intell. J. 142(1–2), 157–170 (2002)
A. Jøsang, Conditional reasoning with subjective logic. J. Multiple-Valued Log. Soft Comput. 15(1), 5–38 (2009)
A. Jøsang, Subjective Logic: A Formalism for Reasoning Under Uncertainty (Springer, Cham, 2016)
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. 48–64
A. Jøsang, R. Hayward, S. Pope, Trust network analysis with subjective logic, in Proceedings of the 29th Australasian Computer Science Conference, Hobart (2006), pp. 85–94
A. Jøsang, J. Diaz, M. Rifqi, Cumulative and averaging fusion of beliefs. Inf. Fusion 11(2), 192–200 (2010)
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. 68–83
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)
S. Kotz, N. Balakrishnan, N.L. Johnson, Continuous Multivariate Distributions, vol. 1 (Wiley, New York, 2000)
T.R. Levine, Encyclopedia of Deception (SAGE Publications, Los Angeles, 2014)
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)
T.K. Moon, The expectation-maximization algorithm. IEEE Signal Process. Mag. 13(6), 47–60 (1996)
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. 49–65
N. Oren, T.J. Norman, A. Preece, Subjective logic and arguing with evidence. Artif. Intell. 171(10), 838–854 (2007)
J. Pasternack, D. Roth, Making better informed trust decisions with generalized fact-finding, in IJCAI (Spatial Cognition, Bremen, 2011), pp. 2324–2329
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. 1206–1212
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. 326–347
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)
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. 558–565
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. 138–145
M. Şensoy, B. Yilmaz, T.J. Norman, Stage: stereotypical trust assessment through graph extraction. Comput. Intell. 32(1), 72–101 (2016)
G. Shafer, A Mathematical Theory of Evidence (Princeton University Press, Princeton, 1976)
P. Smets, The combination of evidence in the transferable belief model. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 447–458 (1990)
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)
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)
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. 336–344
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)
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)
D. Wang, T. Abdelzaher, L. Kaplan, Social Sensing: Building Reliable Systems on Unreliable Data (Morgan Kaufmann, Waltham, 2015)
A. Whitby, A. Jøsang, J. Indulska, Filtering out unfair ratings in Bayesian reputation systems. ICFAIN J. Manag. Res. 4(2), 48–64 (2005)
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. 467–476
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)
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. 73–80
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kaplan, L., Şensoy, M. (2019). Uncertainty Characterization and Fusion of Information from Unreliable Sources. In: Bossé, É., Rogova, G. (eds) Information Quality in Information Fusion and Decision Making. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-030-03643-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-03643-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03642-3
Online ISBN: 978-3-030-03643-0
eBook Packages: Computer ScienceComputer Science (R0)