Partial observable update for subjective logic and its application for trust estimation

Kaplan, Lance; Şensoy, Murat; Chakraborty, Supriyo; Mel, Geeth de

doi:10.1016/j.inffus.2015.01.005

Cited by 9 publications

(6 citation statements)

References 46 publications

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“…Equally interesting is the inverse problem. Since trust and biases modulate propagation of information, observing the propagation patterns themselves reveals the underlying trust relations and biases of sources [7]. This effect is demonstrated in Figure 3 showing information dissemination topologies among sources supporting different candidates in an Egyptian election.…”

Section: Understanding Communities Social Trust and Polarizationmentioning

confidence: 95%

“…In such cases, the estimator must take into account the degree of confidence that a source expresses in its messages to make proper assessments. Recent work based on subjective logic, a type of uncertain probabilistic logic, has developed a framework to assess source reliability in such situations [7]. This framework presumes that the expressions of vagueness are quantified as specific probabilities.…”

Section: Quantifying Data Reliability and Performance Boundsmentioning

confidence: 99%

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The Age of Social Sensing

et al. 2019

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Online social media, such as Twitter and Instagram, democratized information broadcast, allowing anyone to share information about themselves and their surroundings at an unprecedented scale. The large volume of information thus posted on these media offer a new lens into the physical world through the eyes of the social network. The exploitation of this lens to inspect aspects of world state has recently been termed social sensing. The power of manipulating reality via the use (or intentional misuse) of social media opened concerns with issues ranging from radicalization by terror propaganda to potential manipulation of elections in mature democracies. Many important challenges and open research questions arise in this emerging field that aims to better understand how information can be extracted from the medium and what properties characterize the extracted information and the world it represents. Addressing the above challenges requires multi-disciplinary research at the intersection of computer science and social sciences that combines cyber-physical computing, sociology, sensor networks, social networks, cognition, data mining, estimation theory, data fusion, information theory, linguistics, machine learning, behavioral economics, and possibly others. This paper surveys important directions in social sensing, identifies current research challenges, and outlines avenues for future research.

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Section: Understanding Communities Social Trust and Polarizationmentioning

confidence: 95%

Section: Quantifying Data Reliability and Performance Boundsmentioning

confidence: 99%

The Age of Social Sensing

et al. 2019

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“…This is done for various values of γ ∈ [0, 1], and the plot of the actualγ and the desired γ should follow a straight line as it should be the case thatγ ≈ γ. A more detailed discussion can be found in [14]. The quality of the inferred subjective opinion ω X should be judged on how well its expression of uncertainty captures the spread between its projected probability and the actual ground truth probability.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, with 15% of the imports coming from Germany, the UK economy is also significantly dependent on the German market 13 (instead Germany imports mostly from the Netherlands and exports mostly to the US). 14 Finally, the MSCI European Index return is heavily affected by Germany, the first economy in the European Union. 3.…”

Section: Istanbul Stock Market Predictionsmentioning

confidence: 99%

Subjective Bayesian Networks and Human-in-the-Loop Situational Understanding

Braines

Thomas

Kaplan

et al. 2018

Lecture Notes in Computer Science

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In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well as identify gaps in information. This process for comprehension of the meaning information requires the ability to reason inductively, for which we will exploit the machines' ability to 'learn' from data. However, important phenomena are often rare in occurrence with high degrees of uncertainty, thus severely limiting the availability of instance data for training, and hence the applicability of many machine learning approaches. Therefore, we present the benefits of Subjective Bayesian Networks-i.e., Bayesian Networks with imprecise probabilities-for situational understanding, and the role of conversational interfaces for supporting decision makers in the evolution of situational understanding.

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“…Subjective logic has been extended to include belief updates from partially visible evidence (Kaplan, Sensoy, Chakraborty, & de Mel, 2015). Lance et al show that assets of the partial observable update as a function of the state likelihood and demonstrate the use of these likelihoods for a trust estimation application.…”

Section: Related Workmentioning

confidence: 99%

Reasoning with streamed uncertain information from unreliable sources

Arunkumar

Şensoy

Srivatsa

et al. 2015

Expert Systems with Applications

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a b s t r a c tHumans or intelligent software agents are increasingly faced with the challenge of making decisions based on large volumes of streaming information from diverse sources. Decision makers must process the observed information by inferring additional information, estimating its reliability and orienting it for decision-making. In this paper, we propose a stream-reasoning framework that achieves all these goals. While information is streamed as unstructured reports (e.g., text in natural language) from unreliable sources, our framework first converts it into a structured form using Controlled English and then it derives some facts that are useful for decision-making, and estimates the trust in these facts. Lastly, various facts are fused based on their trustworthiness. This process is totally undertaken on streaming information resulting in new facts being inferred from incoming information which immediately goes through trust assessment framework and trust is propagated to the inferred fact. In this paper, we propose a comprehensive framework where unstructured reports are streamed from heterogeneous and potentially untrustworthy information sources. These reports are processed to extract valuable uncertain information, which is represented using controlled natural language and subjective logic. Additional information is inferred using deduction and abduction operations over subjective opinions derived from the reports. Before fusing extracted and inferred opinions, the framework estimates trustworthiness of these opinions, detects conflicts between them, and resolve these conflicts by analysing evidence about the reliability of their sources. Lastly, we describe an implementation of the framework using International Technology Alliance (ITA) assets (Information Fabric Services and Controlled English Fact Store) and present an experimental evaluation that quantifies the efficiency with respect to accuracy and overhead of the proposed framework.

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Partial observable update for subjective logic and its application for trust estimation

Cited by 9 publications

References 46 publications

The Age of Social Sensing

The Age of Social Sensing

Subjective Bayesian Networks and Human-in-the-Loop Situational Understanding

Reasoning with streamed uncertain information from unreliable sources

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