Abstract
This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping it onto the fascinating paradox of trying to identify stochastic liars without any additional information about the truth. Even though that work was significant, it was constrained by the model in which we are living in a world where “the truth prevails over lying”. Couched in the terminology of Learning Automata (LA), this corresponds to the Environment (Since the Environment is treated as an entity in its own right, we choose to capitalize it, rather than refer to it as an “environment”, i.e., as an abstract concept.) being “Stochastically Informative”. However, as explained in the paper, solving the problem under the condition that the Environment is “Stochastically Deceptive”, as opposed to informative, is far from trivial. In this paper, we provide a solution to the problem where the Environment is deceptive (We are not aware of any other solution to this problem (within this setting), and so we believe that our solution is both pioneering and novel.), i.e., when we are living in a world where “lying prevails over the truth”.
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B.J. Oommen—Chancellor’s Professor; Fellow: IEEE and Fellow: IAPR. This author is also an Adjunct Professor with the University of Agder in Grimstad, Norway.
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Notes
- 1.
This being said, the content and goal of this paper is to present a solution within a theoretical and conceptual framework. Thus, we will not embark on the study of any real-life application domains here.
- 2.
In the case of a recommendation system, a Deceptive Environment can, for example, correspond to a compromised system where the integrity of the majority of the agents in the systems are compromised.
- 3.
This assumption, however, does not simplify the problem. Indeed, \(p_R\) can be assigned to be the smallest value of all the values of \(p_i\) for the reliable sensors, and \(p_U\) can be assigned to be the largest value of all the values of \(p_i\) for the unreliable ones.
- 4.
Throughout this paper, since we will be invoking majority-like decisions, we assume that \(N=N_R+N_U\) is an even number.
- 5.
The absence of convergence was also supported by experimental results that are not reported here. This was, indeed, what motivated the present avenue of research.
References
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Yazidi, A., Oommen, B.J., Goodwin, M.: On solving the problem of identifying unreliable sensors without a knowledge of the ground truth: the case of stochastic environments. IEEE Trans. Cybernet. 47, 1604–1617 (2017)
Yazidi, A., Oommen, B.J., Goodwin, M.: On distinguishing between reliable and unreliable sensors without a knowledge of the ground truth. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 2, pp. 104–111. IEEE (2015)
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Yazidi, A., Oommen, B.J., Goodwin, M. (2017). Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_52
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DOI: https://doi.org/10.1007/978-3-319-69179-4_52
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