Predictive Reasoning in Subjective Bayesian Networks

Subjective Bayesian networks extend Bayesian networks by substituting the conditional probability distributions with subjective opinions. In that way they enable explicit representation of the uncertainty in the probabilistic information encoded in the network. In this paper we focus on predictive reasoning in subjective Bayesian networks and propose an inference method that is based on the operations of deduction and multiplication of subjective opinions. We demonstrate modelling and inference with subjective Bayesian networks through an example.

Trust transitivity and conditional belief reasoning

Abstract. Trust transitivity is a common phenomenon embedded in human reasoning about trust. Given a specific context or purpose, trust transitivity is often manifested through the humans' intuition to rely on the recommendations of a trustworthy advisor about another entity that the advisor recommends. Although this simple principle has been formalised in various ways for many trust and reputation systems, there is no real or physical basis for trust transitivity to be directly translated into a mathematical model. In that sense, all mathematical operators for trust transitivity proposed in the literature must be considered ad hoc; they represent attempts to model a very complex human phenomenon as if it were lendable to analysis by the laws of physics. Considering this nature of human trust transitivity in reality, any simple mathematical model will essentially have rather poor predictive power. In this paper, we propose a new interpretation of trust transitivity that is radically different from those described in the literature so far. More specifically, we consider recommendations from an advisor as evidence that the relying party will use as input arguments in conditional reasoning models for assessing hypotheses about the trust target. The proposed model of conditional trust transitivity is based on the framework of subjective logic

Інтегрування гетерогенних геопросторових даних на основі теорії свідчень Демпстера-Шейфера

У статті розглядається підхід до інтегрування гетерогенних геопросторових даних на базі єдиної науково-методичної платформи – теорії свідчень Демпстера-Шейфера. Наведено структурно-логічну схему інтегрування гетерогенних геопросторових даних, суть якої зводиться до послідовного інтегрування таких даних на трьох базових рівнях: фізичному, логіко-семантичному та прийняття рішень. Проаналізовано особливості інтегрування гетерогенних геопросторових даних на фізичному, логіко-семантичному рівнях та специфіку їх інтегрування на рівні гіпотез (рішень) у рамках теорії свідчень Демпстера-Шейфера, яка полягає у необхідності релевантного опису конфлікту, що потенційно може виникнути при наявності суперечливих свідчень (гіпотез) та отриманні інтервальної оцінки ймовірності настання події, що у свою чергу передбачає розробки або обрання певних правил, які дозволяють її точно розрахувати у визначеному інтервалі.В статье рассматривается подход к интегрированию гетерогенных геопространственных данных на базе единой научно-методической платформы – теории свидетельств Демпстера-Шейфера. Приведена структурно-логическая схема интегрирования гетерогенных геопространственных данных, суть которой сводится к последовательному интегрированию таких данных на трех базовых уровнях: физическом, логико-семантическом и принятия решений. Проанализированы особенности интегрирования гетерогенных геопространственных данных на физическом, логико-семантическом уровнях, а также специфика их интегрирования на уровне гипотез (решений) в рамках теории свидетельств Демпстера-Шейфера, которая заключается в необходимости релевантного описания конфликта, который потенциально может возникнуть при наличии противоречивых свидетельств (гипотез) и получении интервальной оценки вероятности наступления события, что в свою очередь предусматривает разработки или выбор определенных правил, которые позволяют ее точно рассчитать в определенном интервале.The article deals with an approach towards integration of geospatial data based on the unified research and methodical platform – the Dempster-Shafer theory of evidence. The article presents a structural chart of heterogeneous geospatial data integration which enables a consecutive integration of such data at three basic levels: physical, logical-semantic and decision-making. It analyzes peculiarities of heterogeneous geospatial data integration at physical and logical-semantic levels as well as particulars of their integration at the level of hypotheses (decisions) within the Dempster-Shafer theory of evidence which envisages a relevant conflict description that can potentially break out in case of controversial evidences (hypotheses) as well as obtaining interval evaluation of the event likelihood of occurrence, which, in its turn, requires development and selection of certain rules allowing its precise calculation in the determined interval

Towards a Model of Argument Strength for Bipolar Argumentation Graphs

UID/FIL/00183/2013Bipolar argument graphs represent the structure of complex pro and contra arguments for one or more standpoints. In this article, ampliative and exclusionary principles of evaluating argument strength in bipolar acyclic argumentation graphs are laid out and compared to each other. Argument chains, linked arguments, link attackers and supporters, and convergent arguments are discussed. The strength of conductive arguments is also addressed but it is argued that more work on this type of argument is needed to properly distinguish argument strength from more general value-based components of such argu- ments. The overall conclusion of the article is that there is no justifiably unique solution to the problem of argument strength outside of a particular epistemological framework.publishersversionpublishe

Fake News Detection Based on Subjective Opinions

Fake news fluctuates social media, leading to harmful consequences. Several types of information could be utilized to detect fake news, such as news content features and news propagation features. In this study, we focus on the user spreading news behaviors on social media platforms and aim to detect fake news more effectively with more accurate data reliability assessment. We introduce Subjective Opinions into reliability evaluation and proposed two new methods. Experiments on two popular real-world datasets, BuzzFeed and PolitiFact, validates that our proposed Subjective Opinions based method can detect fake news more accurately than all existing methods, and another proposed probability based method achieves state-of-art performance

SLFTD: a subjective logic based framework for truth discovery

Finding truth from various conflicting candidate values provided by different data sources is called truth discovery, which is of vital importance in data integration. Several algorithms have been proposed in this area, which usually have similar procedure: iterativly inferring the truth and provider's reliability on providing truth until converge. Therefore, an accurate provider's reliability evaluation is essential. However, no work pays attention to ``how reliable this provider continuously providing truth". Therefore, we introduce subjective logic, which can records both (1) the provider's reliability of generating truth, and (2) reliability of provider continuously doing so. Our proposed methods provides a better evaluation for data providers, and based which, truth are discovered more accurately. Our framework can handle both categorical and numerical data, and can identify truth in either a generative or discriminative way. Experiments on two popular real world datasets, Book and Population, validates that our proposed subjective logic based framework can discover truth much more accurately than state-of-art methods

Recommendation Framework Based on Subjective Logic in Decision Support Systems

In this thesis our goals are to investigate the suitability of subjective logic within the decision support context that requires connectivity to complex data, user specification of frames of discernment, representation of complex reasoning expressions, an architecture that supports distributed usage of a decision support tool based on a client-server approach that separates user interactions on the browser side from computational engines for calculations on the server side, and analysis of the suitability and limitations of the proposed architecture

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

Subjective Logic (SL) is a type of probabilistic logic, which is suitable for reasoning about situations with uncertainty and incomplete knowledge. In recent years, SL has drawn a significant amount of attention from the multi-agent systems community as it connects beliefs and uncertainty in propositions to a rigorous statistical characterization via Dirichlet distributions. However, one serious limitation of SL is that the belief updates are done only based on completely observable evidence. This work extends SL to incorporate belief updates from partially observable evidence. Normally, the belief updates in SL presume that the current evidence for a proposition points to only one of its mutually exclusive attribute states. Instead, this work considers that the current attribute state may not be completely observable, and instead, one is only able to obtain a measurement that is statistically related to this state. In other words, the SL belief is updated based upon the likelihood that one of the attributes was observed. The paper then illustrates properties of the partial observable updates as a function of the state likelihood and illustrates the use of these likelihoods for a trust estimation application. Finally, the utility of the partial observable updates is demonstrated via various simulations including the trust estimation case.U.S. Army Research Laboratory ; U.K. Ministry of Defence ; TÜBİTAKpre-prin