Daniel Pacholczyk

A Symbolic Approach To Uncertainty Management

In this paper, we present a new symbolic approach to deal with the uncertainty encountered in common-sense reasoning. This approach enables us to represent the uncertainty by using linguistic expressions of the interval [Certain, Totally uncertain]. The original uncertainty scale that we use here, presents some advantages over other scales in the representation and in the management of the uncertainty. The axioms of our theory are inspired by Shannons entropy theory and built on the substrate of a symbolic many-valued logic. So, the uncertainty management in the symbolic logic framework leads to new generalizations of classical inference rules.

An Intelligent System Dealing with Negative Information

In this paper, we present an Intelligent System dealing with Linguistic Negation of Nuanced Properties. It is basically founded upon a Similarity Relation between Properties through their corresponding Fuzzy Sets. With the aid of an Interactive Choice Strategy, a User can explain more explicitly the intended meaning of the Negative Information. This Model improves the abilities in Knowledge Management in that premise or conclusion of a Rule can include Linguistic Negations.

Towards a Symbolic Interpretation of Approximate Reasoning

Abstract We study knowledge-based systems using symbolic many-valued logic and we focus on the management of knowledge through linguistic concepts characterized by vague terms or labels. In previous papers we have proposed a symbolic representation of nuanced statements. In this representation, we have interpreated some nuances of natural language as linguistic modifiers and we have defined them within a multiset context. In this paper, we continue the presentation of our symbolic model and we propose new deduction rules dealing with nuanced statements. We limit ourself to present new generalizations of the Modus Ponens rules dealing with nuanced statements.

A Fuzzy Analysis of Linguistic Negation of Nuanced Property in Knowledge-Based Systems

In this paper, we present a Pragmatic Model dealing with Linguistic Negation in Knowledge-Based Systems. It can be viewed as a Generalization of Linguistic Approach to Negative Information within a Fuzzy Context. We define the Linguistic Negation with the aid of a Similarity Relation between Nuanced Properties. We propose a Choice Strategy allowing the User to explain the intended Meaning of Linguistic Negations. This Model improves the abilities in Management of a Knowledge Base, since Information can refer to Linguistic Negations either in Facts or in Rules.

Qualitative reasoning under ignorance and information-relevant extraction

Abstract. This paper is devoted to qualitative reasoning under ignorance. We show how to represent conditional ignorance and informational relevance in the symbolic entropy theory that we have developed in our previous work. This theory allows us to represent uncertainty, in the ignorance form, as in common-sense reasoning, by using the linguistic expressions of the interval [Certain, Completely uncertain]. We recall this theory, then we introduce the notions of conditional ignorance and of informational relevance. Finally we present some theorems of qualitative reasoning with uncertain knowledge. Particularly, we show how to extract the best relevant information in order to treat some problems under ignorance.

A new approach to linguistic negation of nuanced information in knowledge-based systems

In this paper, we focus our attention on the representation of linguistic negation of nuanced information. The new model presented here parses the standard forms of linguistic negation and defines its nuanced strength with the aid of a compatibility level and tolerance threshold. Their combination allows us to choose the reference frame from which the possible values of a linguistic negation of A appearing in the statement “x is not A” will be extracted. Moreover, a choice strategy computes the intended meaning of each linguistic negation.

A Qualitative Approach to Syllogistic Reasoning

In this paper we present a new approach to a symbolic treatment of quantified statements having the following form “QAs are Bs”, knowing that A and B are labels denoting sets, and Q is a linguistic quantifier interpreted as a proportion evaluated in a qualitative way. Our model can be viewed as a symbolic generalization of statistical conditional probability notions as well as a symbolic generalization of the classical probabilistic operators. Our approach is founded on a symbolic finite M-valued logic in which the graduation scale of M symbolic quantifiers is translated in terms of truth degrees. Moreover, we propose symbolic inference rules allowing us to manage quantified statements.

An Extension of a Linguistic Negation Model Allowing Us to Deny Nuanced Property Combinations

In this paper, we improve the abilities of a previous model of representation of vague information expressed under an affirmative or negative form. The study more especially insists on information referring to linguistic negation. The extended definition of the linguistic negation that we propose makes it possible to deny nuanced property combinations based upon disjunction and conjunction operators. The properties that this linguistic negation possesses now allow considering it as a generalization of the logical one, and this is in satisfactory agreement with linguistic analysis.

Qualitative Reasoning under Uncertain Knowledge

In this paper, we focus our attention on the processing of the uncertainty encountered in the common sense reasoning. Firstly, we explore the uncertainty concept and then we suggest a new approach which enables a representation of the uncertainty by using linguistic values. The originality of our approach is that it allows to reason on the uncertainty interval [[Certain, Totally uncertain]] The uncertainty scale that we use here, presents some advantages over other scales in the representation and in the management of the uncertainty. The axiomatic of our approach is inspired by the Shannon theory of entropy and built on the substrate of a symbolic many-valued logic.

About Linguistic Negation of Nuanced Property in Declarative Modeling in Image Synthesis

In this paper, we focus our attention on the explicit Representation of Linguistic Negation of Nuanced Properties. Our Approach is based upon a Similarity Relation between Nuanced Properties through their corresponding Fuzzy Sets. We propose a Choice Strategy improving the abilities in Declarative Modeling in Image Synthesis. The Designer can refer to Linguistic Negations both in Scene Descriptions and in Validation Rules of potential Solutions, since the Interactive Choice Strategy allows him to explain their implicit meanings.