Browsing by Author "Tang, Y."

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    Reasoning about uncertain information and conflict resolution through trust revision
    (International Foundation for Autonomous Agents and Multiagent Systems, 2013) Şensoy, Murat; Fokoue, A.; Pan, J. Z.; Norman, T. J.; Tang, Y.; Oren, N.; Sycara, K.; Computer Science; ŞENSOY, Murat
    In information driven MAS, information consumers collect information about their environment from various sources such as sensors. However, there is no guarantee that a source will provide the requested information truthfully and correctly. Even if information is provided only by trustworthy sources, it can contain con-flicts that hamper its usability. In this paper, we propose to exploit such conflicts to revise trust in information. This requires a reasoning mechanism that can accommodate domain constraints, uncertainty, and trust. Our formalism — SDL-Lite— is an extension of a tractable subset of Description Logics with Dempster-Shafer theory of evidence. SDL-Lite allows reasoning about uncertain information and enables conflict detection. Then, we propose methods for conflict resolution through trust revision and analyse them through simulations. We show that the proposed methods allow reasonably accurate estimations of trust in information in realistic settings.
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    Reasoning under uncertainty: variations of subjective logic deduction
    (IEEE, 2013) Kaplan, L. M.; Şensoy, Murat; Tang, Y.; Chakraborty, S.; Bisdikian, C.; de Mel, G.; Computer Science; ŞENSOY, Murat
    This work develops alternatives to the classical subjective logic deduction operator. Given antecedent and consequent propositions, the new operators form opinions of the consequent that match the variance of the consequent posterior distribution given opinions on the antecedent and the conditional rules connecting the antecedent with the consequent. As a result, the uncertainty of the consequent actually map to the spread for the probability projection of the opinion. Monte Carlo simulations demonstrate this connection for the new operators. Finally, the work uses Monte Carlo simulations to evaluate the quality of fusing opinions from multiple agents before and after deduction.
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    Conference paperPublicationOpen Access
    Reasoning with uncertain information and trust
    (SPIE, 2013) Şensoy, Murat; Mel, G. de; Fokoue, A.; Norman, T. J.; Pan, J. Z.; Tang, Y.; Oren, N.; Sycara, K.; Kaplan, L.; Pham, T.; Computer Science; ŞENSOY, Murat
    A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, they do not provide an explicit means for reasoning about second order uncertainty. Dempster-Shafer theory of evidence (DST) overcomes this weakness and provides means to fuse and reason about uncertain information. In this paper, we combine DL-Lite with DST to allow scalable reasoning over uncertain semantic knowledge bases. Furthermore, our formalism allows for the detection of conflicts between the fused information and domain constraints. Finally, we propose methods to resolve such conflicts through trust revision by exploiting evidence regarding the information sources. The effectiveness of the proposed approaches is shown through simulations under various settings.