Improved Dynamic Subjective Logic Model with Evidence Driven
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
In Jøsang’s subjective logic, the fusion operator is not able to fuse three or more opinions at a time and it cannot consider the effect of time factors on fusion. Also, the base rate (a) and non-informative prior weight (C) could not change dynamically. In this paper, we propose an Improved Subjective Logic Model with Evidence Driven (ISLM-ED) that expands and enriches the subjective logic theory. It includes the multi-agent unified fusion operator and the dynamic function for the base rate (a) and the non-informative prior weight (C) through the changes in evidence. The multi-agent unified fusion operator not only meets the commutative and associative law but is also consistent with the researchers’s cognitive rules. A strict mathematical proof was given by this paper. Finally, through the simulation experiments, the results show that the ISLM-ED is more reasonable and effective and that it can be better adapted to the changing environment.
Abstract
In Jøsang’s subjective logic, the fusion operator is not able to fuse three or more opinions at a time and it cannot consider the effect of time factors on fusion. Also, the base rate (a) and non-informative prior weight (C) could not change dynamically. In this paper, we propose an Improved Subjective Logic Model with Evidence Driven (ISLM-ED) that expands and enriches the subjective logic theory. It includes the multi-agent unified fusion operator and the dynamic function for the base rate (a) and the non-informative prior weight (C) through the changes in evidence. The multi-agent unified fusion operator not only meets the commutative and associative law but is also consistent with the researchers’s cognitive rules. A strict mathematical proof was given by this paper. Finally, through the simulation experiments, the results show that the ISLM-ED is more reasonable and effective and that it can be better adapted to the changing environment.
참고문헌 (17)
- J. F. Tian, "Actuality and development of trust model", Journal of Hebei University(Natural Science Edition), 30(5), pp.555~560 (2011)
- A. Jøsang, "A logic for uncertain probabilities", International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(3), pp.279~311 (2001)
- A. Jøsang, "Trust network analysis with subjective logic", Proceedings of the 29th Australasian Computer Science Conference (ACSC2006), pp.85~94 (2006)
- A. Jøsang, "A survey of trust and reputation systems for online service provision", Decision Support Systems, 43(2), pp.618~644 (2007)
- A. Jøsang, "Conditional reasoning with subjective logic", Journal of Multiple-Valued Logic and Soft Computing, 15(1), pp.5~38 (2008)
- A. Jøsang, "Trust, Privacy and Security in Digital Business", pp.105~114 (2009), Springer
- A. Jøsang, "Cumulative and averaging fusion of beliefs", Information Fusion, 11(2), pp.192~200 (2010)
- A. Jøsang, "Redefining material implication with subjective logic", Proceedings of the 14th International Conference on Information Fusion (FUSION), pp.1~6 (2011)
- C. L. Huang, "Extension of subjective logic for time-related trust", Wuhan University Journal of Natural Sciences, 10(1), pp.56~60 (2005)
- J. Yuan, "Subjective logic-based anomaly detection framework in wireless sensor networks", International Journal of Distributed Sensor Networks, 2012, pp.1~13 (2012)
- J. Tian, "Trust model based on the multinomial subjective logic and risk mechanism for P2P network of file sharing", Journal of Electronics(China), 28(1), pp.108~117 (2011)
- J. Wang, "A novel subjective logic for trust management", Journal of Computer Research and Development, 47(1), pp.140~146 (2010)
- H. Zhou, "Using new fusion operations to improve trust expressiveness of subjective logic", Wuhan University Journal of Natural Sciences, 16(5), pp.376~382 (2011)
- L. Festinger, "A Theory of Cognitive Dissonance" (1957), Stanford University Press
- Y. P. Jia, "The Research on Fusion for Target Recognition based on Belief Function Theory", pp.2009
- "Subjective logic" (2013)[URL]
- M. Tavallaee, "A detailed analysis of the KDD CUP 99 data set", Proceedings of the 2nd IEEE Symposium on Computational Intelligence for Security and Defence Applications (CISDA) (2009)
