			Home About us Contact

Possibility Distribution (possibility + distribution)

Distribution by Scientific Domains

Information Science and Computing	75%

Selected Abstracts

About yes/no queries against possibilistic databases

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 7 2007
Patrick Bosc
This article is concerned with the handling of imprecise information in databases. The need for dealing with imprecise data is more and more acknowledged in order to cope with real data, even if commercial systems are most of the time unable to manage them. Here, the possibilistic setting is taken into consideration because it is less demanding than the probabilistic one. Then, any imprecise piece of information is modeled as a possibility distribution intended for constraining the more or less acceptable values. Such a possibilistic database has a natural interpretation in terms of a set of regular databases, which provides the basic gateway to interpret queries. However, if this approach is sound, it is not realistic, and it is necessary to consider restricted queries for which a calculus grounded on the possibilistic database, that is, where the operators work directly on possibilistic relations, is feasible. Extended yes/no queries are dealt with here, where their general form is: "to what extent is it possible and certain that tuple t (given) belongs to the answer to Q," where Q is an algebraic relational query. A strategy for processing such queries efficiently is proposed under some assumptions as to the operators appearing in Q. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 691,721, 2007. [source]

A CASE-BASED DECISION SUPPORT SYSTEM FOR INDIVIDUAL STRESS DIAGNOSIS USING FUZZY SIMILARITY MATCHING

COMPUTATIONAL INTELLIGENCE, Issue 3 2009
Shahina Begum
Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty-nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process. [source]

Incorporating linguistic, probabilistic, and possibilistic information in a risk-based approach for ranking contaminated sites

INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT, Issue 4 2010
Kejiang Zhang
Abstract Different types of uncertain information,linguistic, probabilistic, and possibilistic,exist in site characterization. Their representation and propagation significantly influence the management of contaminated sites. In the absence of a framework with which to properly represent and integrate these quantitative and qualitative inputs together, decision makers cannot fully take advantage of the available and necessary information to identify all the plausible alternatives. A systematic methodology was developed in the present work to incorporate linguistic, probabilistic, and possibilistic information into the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), a subgroup of Multi-Criteria Decision Analysis (MCDA) methods for ranking contaminated sites. The identification of criteria based on the paradigm of comparative risk assessment provides a rationale for risk-based prioritization. Uncertain linguistic, probabilistic, and possibilistic information identified in characterizing contaminated sites can be properly represented as numerical values, intervals, probability distributions, and fuzzy sets or possibility distributions, and linguistic variables according to their nature. These different kinds of representation are first transformed into a 2-tuple linguistic representation domain. The propagation of hybrid uncertainties is then carried out in the same domain. This methodology can use the original site information directly as much as possible. The case study shows that this systematic methodology provides more reasonable results. Integr Environ Assess Manag 2010;6:711,724. © 2010 SETAC [source]

Towards an automated deduction system for first-order possibilistic logic programming with fuzzy constants

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 9 2002
Teresa Alsinet
In this article, we present a first-order logic programming language for fuzzy reasoning under possibilistic uncertainty and poorly known information. Formulas are represented by a pair (,, ,), in which , is a first-order Horn clause or a query with fuzzy constants and regular predicates, and , , [0, 1] is a lower bound on the belief on , in terms of necessity measures. Since fuzzy constants can occur in the logic component of formulas, the truth value of formulas is many-valued instead of Boolean. Moreover, since we have to reason about the possibilistic uncertainty of formulas with fuzzy constants, belief states are modeled by normalized possibility distributions on a set of many-valued interpretations. In this framework, (1) we define a syntax and a semantics of the underlying logic; (2) we give a sound modus ponens-style calculus by derivation based on a semantic unification pattern of fuzzy constants; (3) we develop a directional fuzzy unification algorithm based on the distinction between general and specific object constants; and (4) we describe a backward first-order proof procedure oriented to queries that is based on the calculus of the language and the computation of the unification degree between fuzzy constants in terms of a necessity measure for fuzzy events. © 2002 Wiley Periodicals, Inc. [source]