8 research outputs found
Consensus in the Presence of Multiple Opinion Leaders: Effect of Bounded Confidence
The problem of analyzing the performance of networked agents exchanging
evidence in a dynamic network has recently grown in importance. This problem
has relevance in signal and data fusion network applications and in studying
opinion and consensus dynamics in social networks. Due to its capability of
handling a wider variety of uncertainties and ambiguities associated with
evidence, we use the framework of Dempster-Shafer (DS) theory to capture the
opinion of an agent. We then examine the consensus among agents in dynamic
networks in which an agent can utilize either a cautious or receptive updating
strategy. In particular, we examine the case of bounded confidence updating
where an agent exchanges its opinion only with neighboring nodes possessing
'similar' evidence. In a fusion network, this captures the case in which nodes
only update their state based on evidence consistent with the node's own
evidence. In opinion dynamics, this captures the notions of Social Judgment
Theory (SJT) in which agents update their opinions only with other agents
possessing opinions closer to their own. Focusing on the two special DS
theoretic cases where an agent state is modeled as a Dirichlet body of evidence
and a probability mass function (p.m.f.), we utilize results from matrix
theory, graph theory, and networks to prove the existence of consensus agent
states in several time-varying network cases of interest. For example, we show
the existence of a consensus in which a subset of network nodes achieves a
consensus that is adopted by follower network nodes. Of particular interest is
the case of multiple opinion leaders, where we show that the agents do not
reach a consensus in general, but rather converge to 'opinion clusters'.
Simulation results are provided to illustrate the main results.Comment: IEEE Transactions on Signal and Information Processing Over Networks,
to appea
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
Intensional Cyberforensics
This work focuses on the application of intensional logic to cyberforensic
analysis and its benefits and difficulties are compared with the
finite-state-automata approach. This work extends the use of the intensional
programming paradigm to the modeling and implementation of a cyberforensics
investigation process with backtracing of event reconstruction, in which
evidence is modeled by multidimensional hierarchical contexts, and proofs or
disproofs of claims are undertaken in an eductive manner of evaluation. This
approach is a practical, context-aware improvement over the finite state
automata (FSA) approach we have seen in previous work. As a base implementation
language model, we use in this approach a new dialect of the Lucid programming
language, called Forensic Lucid, and we focus on defining hierarchical contexts
based on intensional logic for the distributed evaluation of cyberforensic
expressions. We also augment the work with credibility factors surrounding
digital evidence and witness accounts, which have not been previously modeled.
The Forensic Lucid programming language, used for this intensional
cyberforensic analysis, formally presented through its syntax and operational
semantics. In large part, the language is based on its predecessor and
codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective
Lucid, and JOOIP bound by the underlying intensional programming paradigm.Comment: 412 pages, 94 figures, 18 tables, 19 algorithms and listings; PhD
thesis; v2 corrects some typos and refs; also available on Spectrum at
http://spectrum.library.concordia.ca/977460
Intensional Cyberforensics
This work focuses on the application of intensional logic to cyberforensic analysis and its benefits and difficulties are compared with the finite-state-automata approach. This work extends the use of the intensional programming paradigm to the modeling and implementation of a cyberforensics investigation process with backtracing of event reconstruction, in which evidence is modeled by multidimensional hierarchical contexts, and proofs or disproofs of claims are undertaken in an eductive manner of evaluation. This approach is a practical, context-aware improvement over the finite state automata (FSA) approach we have seen in previous work. As a base implementation language model, we use in this approach a new dialect of the Lucid programming language, called Forensic Lucid, and we focus on defining hierarchical contexts based on intensional logic for the distributed evaluation of cyberforensic expressions. We also augment the work with credibility factors surrounding digital evidence and witness accounts, which have not been previously modeled.
The Forensic Lucid programming language, used for this intensional cyberforensic analysis, formally presented through its syntax and operational semantics. In large part, the language is based on its predecessor and codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective Lucid, MARFL, and JOOIP bound by the underlying intensional programming paradigm
Cumulative and Averaging Fusion of Beliefs
International audienceThe problem of fusing beliefs in the Dempster-Shafer belief theory has attracted consid-erable attention over the last two decades. The classical Dempster's Rule has often been criticised, and many alternative rules for belief combination have been proposed in the lit-erature. We show that it is crucial to consider the nature of the situation at hand and to select the appropriate fusion operator as a function thereof. In this paper we present the cu-mulative rule and the averaging rule of belief fusion, which represent generalisations of the subjective logic consensus operator for independent and dependent opinions respectively. The generalised operators are applicable to the combination of general basic belief assign-ments (bbas). These rules, which can be directly derived from classical statistical theory, produce results that correspond well with human intuition