Introducing fuzzy trust for managing belief conflict over semantic web data

Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model

Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators for this multi-agent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating on direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Yager's rule is shown to be the better operator under various parameter values, i.e. convergence to the best state, robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen

Методика визначення думок експертів відносно зрілості безпеки інформації із застосуванням математичного апарату суб’єктивної логіки

Обґрунтовано можливість використання апарату суб‘єктивної логіки для оцінки зрілості систем забезпечення безпеки інформації. Розглядаються алгоритми формування думок у просторі суб’єктивної логіки. Пропонується новий метод, заснований на використанні зон базових думок, та обговорюються особливості його застосування.Using possibility of this mechanism in information security system maturity level evaluating is given. Opinion forming method in space of Subjective Logic is described. New method based on using basis opinion regions is proposed and features of its application are considered

Reaching Consensus with Imprecise Probabilities over a Network

This paper discusses the problem of a distributed network of agents attempting to agree on an imprecise probability over a network. Unique from other related work however, the agents must reach agreement while accounting for relative uncertainties in their respective probabilities. First, we assume that the agents only seek to agree to a centralized estimate of the probabilities, without accounting for observed transitions. We provide two methods by which such an agreement can occur which uses ideas from Dirichlet distributions. The first methods interprets the consensus problem as an aggregation of Dirichlet distributions of the neighboring agents. The second method uses ideas from Kalman Consensus to approximate this consensus using the mean and the variance of the Dirichlet distributions. A key results of this paper is that we show that when the agents are simultaneously actively observing state transitions and attempting to reach consensus on the probabilities, the agreement protocol can be insensitive to any new information, and agreement is not possible. Ideas from exponential fading are adopted to improve convergence and reach a consistent agreement.This research was funded in part under Air Force Grants # F49620-01-1-0453 and # FA9550-08-1-0086

Деякі аспекти фінансово-кредитної безпеки як складової економічної безпеки України

Розглядаються теоретичні аспекти фінансово-кредитної безпеки України. Аналізуються процеси розвитку фінансово-кредитних міжнаціональних відносин, стану інформаційної економіки, демографічні показники на прикладі працездатного населення України. Визначені порогові індикатори критичних показників кредитних ризиків для здійснення реструктуризації боргів. Надаються пропозиції для забезпечення кредитно-фінансової безпеки як складової економічної безпеки України.In the article are considered theoretical aspects of financial-credit security of Ukraine. The processes of development of financial-credit relations between nations, state of informational economy, demographical indexes on the example of work-available population of Ukraine are being analyzed. Shown the extreme indicators of critical indexes of credit risks for caring out of restructorization of debts. Offered the propositions for providing of credit-financial security as a component of economical security of Ukraine

Towards an integrated formal analysis for security and trust

We aim at defining an integrated framework for the (automated) analysis for security and trust in complex and dynamic scenarios. In particular, we show how the same machinery used for the formal verification of security protocols may be used to analyze access control policies based on trust management

Flow-based reputation with uncertainty: Evidence-Based Subjective Logic

The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and Subjective Logic based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, Subjective Logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the `opinion discounting' operation in Subjective Logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent Subjective Logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of both worlds: flow-based reputation and consistent handling of uncertainties