On the Analysis of Probability-Possibility Transformations: Changing Operations and Graphical Models

Benferhat, Salem; Levray, Amélie; Tabia, Karim

doi:10.1007/978-3-319-20807-7_25

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…For possibilistic networks, parameter learning from data consists basically in deriving conditional local possibility distributions from data. There are two main approaches for learning the parameters [Haddad et al, 2015]: i) Transformation-based approach: It first consists in learning probability distributions from data then transforming them into possibilistic ones using probabilitypossibility transformations [Benferhat et al, 2015a]. ii) Possibilistic-based approach: Such approaches stem from some quantitative interpretations of possibility distributions.…”

Section: Fig 8 Example Of a Possibilistic Networkmentioning

confidence: 99%

Belief Graphical Models for Uncertainty Representation and Reasoning

Benferhat

Leray

Tabia

2020

A Guided Tour of Artificial Intelligence Research

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Many real world problems and applications require to exploit incomplete, complex and uncertain information. Belief graphical models encompass a wide range of graphical formalisms for representing and reasoning with uncertain and complex information. They generally involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications.

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Section: Fig 8 Example Of a Possibilistic Networkmentioning

confidence: 99%

Belief Graphical Models for Uncertainty Representation and Reasoning

Benferhat

Leray

Tabia

2020

A Guided Tour of Artificial Intelligence Research

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“…We choose three logical operators to include with the definition: and, or, not. The example in 1 Listing 1: Representing uncertainty with mUnc the listing 1 shows how to assert that a sentence ex:S1 is true with a probability of 0.7. For the sake of illustration, we use reification to attach an IRI to the previous sentence, although no preference about metadata representation methods is stated [16].…”

Section: Munc: a Vocabulary For Uncertainty Theoriesmentioning

confidence: 99%

“…The latter are a probability distribution of possibility distributions. An interesting analysis of the possibility-probability transformation and its links to graphical models can be found in [1].…”

Section: Translating Uncertainty Between Theoriesmentioning

confidence: 99%

Linking and Negotiating Uncertainty Theories Over Linked Data

Djebri

Tettamanzi

Gandon

2019

Companion Proceedings of the 2019 World Wide Web Conference

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There is no credibility insurance measure for the information provided by the Web. In most cases, information cannot be checked for accuracy. Semantic Web technologies aimed to give structure and sense to information published on the Web and to provide us with a machine-readable data format for interlinked data. However, Semantic Web standards do not offer the possibility to represent and attach uncertainty to such data in a way allowing the reasoning over the latter. Moreover, uncertainty is context-dependent and may be represented by multiple theories which apply different calculi. In this paper, we present a new vocabulary and a framework for handling generic uncertainty representation and reasoning. The meta-Uncertainty vocabulary offers a way to represent uncertainty theories and annotate Linked Data with uncertainty information. We provide the tools to represent uncertainty calculi linked to the previous theories using the LDScript function scripting language. Moreover, we describe the semantics of contexts in uncertainty reasoning with meta-uncertainty. We describe the mapping between RDF triples and their uncertainty information, and we demonstrate the effect on the query writing process in Corese. We discuss the translatability of uncertainty theories and, finally, the negotiation of an answer annotated with uncertainty information. CCS CONCEPTS • Information systems → Uncertainty; Web data description languages; Ontologies; • Theory of computation → Incomplete, inconsistent, and uncertain databases.

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“…And our intention was to compare the results of possibilistic and probabilistic networks on the same data, which means keeping the ordinal information contained in the probabilistic data. Using a least committed probability-to-possibility preference preserving transformation at the local level was a natural way of generating such possibilistic counterparts of subjective probabilistic data, even if we are aware that making local probability-to-possibility transforms is for instance not equivalent to making probability-to-possibility transforms of the joint probability, as studied in [7]. Note that the same issues occur when trying to learn possibilistic networks from data [18].…”

Section: Model Specificationmentioning

confidence: 99%

Uncertain logical gates in possibilistic networks: Theory and application to human geography

Dubois

Fusco

Prade

et al. 2017

International Journal of Approximate Reasoning

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International audiencePossibilistic networks offer a qualitative approach for modeling epistemic uncertainty. Their practical implementation requires the specification of conditional possibility tables, as in the case of Bayesian networks for probabilities. The elicitation of probability tables by experts is made much easier by means of noisy logical gates that enable multidimensional tables to be constructed from the knowledge of a few parameters. This paper presents the possibilistic counterparts of usual noisy connectives (and, or, max, min,. . .). Their interest and limitations are illustrated on an example taken from a human geography model-ing problem. The difference of behavior between probabilistic and possibilistic connectives is discussed in detail. Results in this paper may be useful to bring possibilistic networks closer to applications

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On the Analysis of Probability-Possibility Transformations: Changing Operations and Graphical Models

Cited by 9 publications

References 14 publications

Belief Graphical Models for Uncertainty Representation and Reasoning

Belief Graphical Models for Uncertainty Representation and Reasoning

Linking and Negotiating Uncertainty Theories Over Linked Data

Uncertain logical gates in possibilistic networks: Theory and application to human geography

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