R. A. Marques Pereira

Soft consensus and network dynamics in group decision making

We propose a dynamical network model for consensus reaching in group decision making. The model combines the minimization of a soft measure of collective dissensus and an individual inertial mechanism which emulates opinion changing aversion. Both components of the dynamics are nonlinear. The collective consensual trend corresponds to a process of anisotropic diffusion among the various individual preference structures. The anisotropy is designed so as to outline and enhance the natural group segmentation into homogeneous preference subgroups (weak consensus). The individual inertial mechanism, on the other hand, opposes changes from the original preferences and provides an appropriate framework to deal with preference outliers. We examine in detail the simple case in which each decision maker must choose between only two alternatives. Finally we comment on the possibility of incorporating in the dynamics a form of transitivity constraint regarding the group segmentation. ©1999 John Wiley & Sons, Inc.

On the issue of consistency in dynamical consensual aggregation

In this paper we propose to use the consistency of preferences in order to endogenously assign different weights to decision makers in a consensual dynamics process. For this purpose, we first define a consistency index for preferences expressed by means of fuzzy preference relations. Then we introduce this index in an iterative law for updating the individual preferences. The updating law is formulated in the spirit of some previous papers on consensual dynamics (see for instance [5]) and, as a result, both the initially declared preferences and the present ones (in the ongoing process) act with a strength determined on the basis of their consistency.

A dynamical model for reaching consensus in group decision making

We propose a dynamical model for reaching consensus in group decision making based on the interplay between a gradual diffusion mechanism and an individual aversion to opinion changing We illustrate our model in the one dimensional case, when each decison maker chooses between only .two alternatives.

Contextual weighted representations and indexing models for the retrieval of HTML documents

The diffusion of the World Wide Web (WWW) and the consequent increase in the production and exchange of textual information demand the development of effective information retrieval systems. The HyperText Markup Language (HTML) constitues a common basis for generating documents over the internet and the intranets. By means of the HTML the author is allowed to organize the text into subparts delimited by special tags; these subparts are then visualized by the HTML browser in distinct ways, i.e. with distinct typographical formats. In this paper a model for indexing HTML documents is proposed which exploits the role of tags in encoding the importance of their delimited text. Central to our model is a method to compute the significance degree of a term in a document by weighting the term instances according to the tags in which they occur. The indexing model proposed is based on a contextual weighted representation of the document under consideration, by means of which a set of (normalized) numerical weights is assigned to the various tags containing the text. The weighted representation is contextual in the sense that the set of numerical weights assigned to the various tags and the respective text depend (other than on the tags themselves) on the particular document considered. By means of the contextual weighted representation our indexing model reflects not only the general syntactic structure of the HTML language but also the information conveyed by the particular way in which the author instantiates that general structure in the document under consideration. We discuss two different forms of contextual weighting: the first is based on a linear weighted representation and is closer to the standard model of universal (i.e. non contextual) weighting; the second is based on a more complex non linear weighted representation and has a number of novel and interesting features.

RULE CORRELATION AND CHOQUET INTEGRATION IN FUZZY INFERENCE SYSTEMS

We propose an extension of the Takagi-Sugeno-Kang (TSK) fuzzy inference system, using Choquet integration for aggregating the single rule outputs. In the new Choquet-TSK fuzzy inference system, the pairwise synergies between rules are encoded in a rule correlation matrix computed from the activation pattern of the rule base. The rule correlation matrix is then used to modulate the parameters of the Choquet integration scheme in order to compensate for the effect of rule synergies, which are present in most rule bases to a higher or lesser extent. The standard TSK fuzzy inference system remains a particular instance of the proposed Choquet-TSK extension and corresponds to the ideal case of rule independence. However, when rule correlation is present, the Choquet-TSK fuzzy inference system takes it into account when computing the final output of the system. On the basis of the rule correlation matrix, the new aggregation scheme of the Choquet-TSK fuzzy inference system attenuates the effective weight of positively correlated rules and emphasizes that of negatively correlated rules. Some case studies are discussed in order to illustrate the proposed methodology.

Choquet Integration and Correlation Matrices in Fuzzy Inference Systems

where we consider the individual weights of the fuzzy rules, wi > 0, to be the normalized activation levels αi for i = 1, . . . , n. In this paper we propose an extension to the TSK fuzzy inference system based on Choquet interation [5, 6], called Choquet–TSK. In our model, a matrix of pairwise correlations among the activation levels of the FIS rules is explicitly used in the aggregation process, leading to attenuation effects when correlations are positive and emphasizing effects when correlations are negative. Consider a finite set of interacting criteria N = {1, 2, . . . , n}. A Choquet measure [1] on the set N is a set function μ : P(N) −→ [0, 1] satisfying

A Cooperating Edge Grammar forEdge Detection

Edge detection is a fundamental process in many low-level vision algorithms. It generates a concise and a compact description of the image structure, suitable for manipulation in computer vision tasks. In this paper we describe an application of Cellular Automata to the reconstruction of edges in an image. The discussed approach is an improvement on the original weak membrane model. Such approach showed good performance when implemented on a massively parallel architecture.

An MCDM Approach to Group Processes Using Choquet Integration

We consider a model of non-additive Choquet aggregation in group processes, with reference to the context of effort estimation in Project Management. The groups considered are formed by “experts” (people with specific technical competence) and “non-experts” (people with less specific technical competence, usually experts in related fields), so that the typically complementary biases of the two classes contribute to a more balanced estimate. We present two simple examples which illustrate how the Choquet aggregation results improve those of the classical weighted mean (additive case). Finally, we discuss the relation between the average Choquet weights of consensual coalitions of experts and non-experts and their individual Shapley values.

Fuzzy Alarm System For Laser Gyroscopes Degradation

Abstract ROSETTA is a European Space Agency (ESA) unrnanned space probe launched in 2004 to study the comet 67P/Churyumov-Gerasimenko. This paper discusses the design of a fuzzy alarm system regarding the intensity degradation of ROSETTA’s laser gyroscopes. The fuzzy alarm system proposed makes use of a novel fuzzy inference scheme, incorporating the technique of Choquet integration. This research work was done in the scope of an ESA project, as aproof-of-concept for a new fuzzy inference scheme.