Truth-Valued-Flow Inference (TVFI) and its applications in approximate reasoning

The framework of the theory of Truth-valued-flow Inference (TVFI) is introduced. Even though there are dozens of papers presented on fuzzy reasoning, we think it is still needed to explore a rather unified fuzzy reasoning theory which has the following two features: (1) it is simplified enough to be executed feasibly and easily; and (2) it is well structural and well consistent enough that it can be built into a strict mathematical theory and is consistent with the theory proposed by L.A. Zadeh. TVFI is one of the fuzzy reasoning theories that satisfies the above two features. It presents inference by the form of networks, and naturally views inference as a process of truth values flowing among propositions

Multiple agent possibilistic logic

International audienceThe paper presents a ‘multiple agent’ logic where formulas are pairs of the form (a, A), made of a proposition a and a subset of agents A. The formula (a, A) is intended to mean ‘(at least) all agents in A believe that a is true’. The formal similarity of such formulas with those of possibilistic logic, where propositions are associated with certainty levels, is emphasised. However, the subsets of agents are organised in a Boolean lattice, while certainty levels belong to a totally ordered scale. The semantics of a set of ‘multiple agent’ logic formulas is expressed by a mapping which associates a subset of agents with each interpretation (intuitively, the maximal subset of agents for whom this interpretation is possibly true). Soundness and completeness results are established. Then a joint extension of the multiple agent logic and possibilistic logic is outlined. In this extended logic, propositions are then associated with both sets of agents and certainty levels. A formula then expresses that ‘all agents in set A believe that a is true at least at some level’. The semantics is then given in terms of fuzzy sets of agents that find an interpretation more or less possible. A specific feature of possibilistic logic is that the inconsistency of a knowledge base is a matter of degree. The proposed setting enables us to distinguish between the global consistency of a set of agents and their individual consistency (where both can be a matter of degree). In particular, given a set of multiple agent possibilistic formulas, one can compute the subset of agents that are individually consistent to some degree

Decision Making Under Ambiguity: A Belief-function Perspective

This is the publisher's version, which is being shared with permission, and which is also available electronically from: http://acs.polsl.pl/In this article, we discuss problems with probability theory in representing uncertainties encountered in the "real world" and show how belief functions can overcome these difficulties. Also, we discuss an expected utility approach of decision making under ambiguity using the belief function framework. In particular, we develop a proposition for decision making under ambiguity using the expected utility theory. This proposition is based on Strat's approach of resolving ambiguity in the problem using belief functions. We use the proposition to explain the Ellsberg paradox and model the decision making behavior under ambiguity. We use the empirical data of Einhorn and Hogarth to validate the proposition. Also, we use the proposition to predict several decision making behaviors under ambiguity for special conditions. Furthermore, we discuss the general condition under which the "switching" behavior, as observed by Einhorn and Hogarth, will occur using the concept of "precision measure" in the expected utility theory

User data discovery and aggregation: the CS-UDD algorithm

In the social web, people use social systems for sharing content and opinions, for communicating with friends, for tagging, etc. People usually have different accounts and different profiles on all of these systems. Several tools for user data aggregation and people search have been developed and protocols and standards for data portability have been defined. This paper presents an approach and an algorithm, named Cross-System User Data Discovery (CS-UDD), to retrieve and aggregate user data distributed on social websites. It is designed to crawl websites, retrieve profiles that may belong to the searched user, correlate them, aggregate the discovered data and return them to the searcher which may, for example, be an adaptive system. The user attributes retrieved, namely attribute-value pairs, are associated with a certainty factor that expresses the confidence that they are true for the searched user. To test the algorithm, we ran it on two popular social networks, MySpace and Flickr. The evaluation has demonstrated the ability of the CS-UDD algorithm to discover unknown user attributes and has revealed high precision of the discovered attributes

Automated reasoning with uncertainties

In this work we assume that uncertainty is a multifaceted concept which admits several different measures, and present a system for automated reasoning with multiple representations of uncertainty. Our focus is on problems which present more than one of these facets and therefore in which a multivalued representation of uncertainty and the study of its possibility of computational realisation are important for designing and implementing knowledge-based systems. We present a case study on developing a computational language for reasoning with uncertainty, starting with a semantically sound and computationally tractable language and gradually extending it with specialised syntactic constructs to represent measures of uncertainty, preserving its unambiguous semantic characterisation and computability properties. Our initial language is the language of normal clauses with SLDNF as the inference rule, and we select three facets of uncertainty, which are not exhaustive but cover many situations found in practical problems: vagueness, statistics and degrees of belief. To each of these facets we associate a specific measure: fuzzy measures to vagueness, probabilities on the domain to statistics and probabilities on possible worlds to degrees of belief. The resulting language is semantically sound and computationally tractable, and admits relatively efficient implementations employing ff \Gamma fi pruning and caching

Gestion de l'incertitude et codage des politiques de sécurité dans les systèmes de contrôle d'accès

La présente thèse s'intéresse à coder la politique de sécurité SELinux en OrBAC et à proposer une extension de ce modèle. Nous avons commencé par présenter l'état de l'art des différents modèles de contrôles d'accès présents dans la littérature en mettant en exergue les limites de chacun de ces modèles. Ensuite nous avons présenté le modèle OrBAC comme étant une extension du modèle RBAC, car d'une part il a apporté la notion de contexte et d'organisation et d'autre part il permet d'exprimer, en plus des permissions, des interdictions et des obligations. Ensuite, nous avons présenté la solution de sécurité SELinux qui utilise un ensemble de modèles de contrôle d'accès comme DAC, RBAC et MAC. Nous avons recensé plusieurs centaines, voire des milliers, de règles dans la politique de sécurité SELinux, ces règles peuvent concerner des décisions d'accès ou des décisions de transition. Nous avons ensuite pu coder lesdites règles en modèle OrBAC, et ce en passant par le remplissage ses tables d'entité, pour ensuite les transformer en relations OrBAC. Notre thèse a aussi rappelé les fondements de la logique possibiliste, et a ensuite apportée une amélioration importante du modèle OrBAC, il s'agit de l'introduction de l'entité priorité au niveau de chaque relation du modèle OrBAC. L'entité priorité quantifie la certitude pour qu'une entité concrète soit injectée dans l'entité abstraite correspondante, ou en cas général, le degré de certitude pour qu'une relation soit réalisée. Nous avons proposé trois modes de combinaison (pessimiste, optimiste et avancé) qui peuvent être adoptés pour déterminer la valeur de la priorité de chaque relation concrète à partir des priorités des relations abstraites correspondantes. Enfin, nous avons implémenté, via une application développé par DELPHI, le codage des règles concernant les décisions d'accès de la politique de sécurité SELinux, en modèle OrBAC tout en introduisant la notion de priorité.This thesis focuses on encoding default-based SELinux security policy in OrBAC and propose an extension of this model. We presented the state of the art of different models of access controls present in the literature underlining the limitations of each of these models. Then we presented the model OrBAC as an extension of the RBAC model, firstly because he brought the notion of context and organization and secondly it allows expressing, in addition to permissions, prohibitions and obligation. Then we presented the SELinux security solution that uses a set of access control models such as DAC, RBAC and MAC. We identified several hundreds or even thousands of rules in SELinux security policy, these rules may be access decisions or decisions of transition. We could then encode these rules in OrBAC model, and via filling its tables of entities, then transform relations OrBAC. Our thesis also reviewed the foundations of possibilistic logic, and then made an important enlargement in OrBAC model; it's to introduce an entity called "priority" in each relationship model OrBAC. The entity "priority" quantifies the certainty for concrete entity injection into the corresponding abstract entity, in general, it's meaning the degree of certainty that a relationship is performed. We proposed three modes of combination (pessimistic, optimistic and advanced) that can be adopted to determine the concrete relations priority value from priorities values of each corresponding abstract relationship. Finally, we implement, via an application developed by DELPHI, coding access decisions rules of the SELinux policy in OrBAC model introducing the priority entity.ARRAS-Bib.electronique (620419901) / SudocSudocFranceF