A New Probabilistic Transformation of Belief Mass Assignment

Dezert, Jean; Smarandache, Florentín

doi:10.2139/ssrn.2725868

Cited by 19 publications

(36 citation statements)

References 8 publications

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“…By example, the pignistic transformation [20], the plausibility transformation [21], the DSmP transformation and other ones presented in [22], etc.…”

Section: Decision Based On Maximum Of Credibilitymentioning

confidence: 99%

Decision-Making with Belief Interval Distance

Dezert¹,

Han

Tacnet³

et al. 2016

Lecture Notes in Computer Science

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“…By example, the pignistic transformation [20], the plausibility transformation [21], the DSmP transformation and other ones presented in [22], etc.…”

Section: Decision Based On Maximum Of Credibilitymentioning

confidence: 99%

Decision-Making with Belief Interval Distance

Dezert¹,

Han

Tacnet³

et al. 2016

Lecture Notes in Computer Science

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“…In this case, a rational decision-maker should approximate the bba by a subjective probability measure, known as pignistic probability measure, based on the underlying frame of discernment. Many pignistic transformations (aka pignistic probabilizations) have been proposed (e.g [54]). Here, we only consider the well-known and widely used transformation defined as [55] …”

Section: Appendix a Some Notions Of The Dempster-shafer Theorymentioning

confidence: 99%

Quantifying leakage in the presence of unreliable sources of information

Hamadou

Palamidessi

Sassone

2017

Journal of Computer and System Sciences

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Belief and min-entropy leakage are two well-known approaches to quantify information flow in security systems. Both concepts stand as alternatives to the traditional approaches founded on Shannon entropy and mutual information, which were shown to provide inadequate security guarantees. In this paper we unify the two concepts in one model so as to cope with the frequent (potentially inaccurate, misleading or outdated) attackers' side information about individuals on social networks, online forums, blogs and other forms of online communication and information sharing. To this end we propose a new metric based on min-entropy that takes into account the adversary's beliefs.

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“…To translate mass functions to probabilities, a number of existing transformation models can be applied such as those in [9,11,32]. One of the most widely used models is Smet's pignistic model [32] which allows decisions to be made from a mass function on individual hypotheses as follows (note that we use |A| to denote the cardinality of a set A): Definition 6 Let m be a mass function over Ω.…”

Section: Preliminariesmentioning

confidence: 99%

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

et al. 2016

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There has been much interest in the belief-desire-intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster-Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster's original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent's belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.

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A New Probabilistic Transformation of Belief Mass Assignment

Cited by 19 publications

References 8 publications

Decision-Making with Belief Interval Distance

Decision-Making with Belief Interval Distance

Quantifying leakage in the presence of unreliable sources of information

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

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