Constraint-wish and satisfied-dissatisfied: an overview of two approaches for dealing with bipolar querying

In recent years, there has been an increasing interest in dealing with user preferences in flexible database querying, expressing both positive and negative information in a heterogeneous way. This is what is usually referred to as bipolar database querying. Different frameworks have been introduced to deal with such bipolarity. In this chapter, an overview of two approaches is given. The first approach is based on mandatory and desired requirements. Hereby the complement of a mandatory requirement can be considered as a specification of what is not desired at all. So, mandatory requirements indirectly contribute to negative information (expressing what the user does not want to retrieve), whereas desired requirements can be seen as positive information (expressing what the user prefers to retrieve). The second approach is directly based on positive requirements (expressing what the user wants to retrieve), and negative requirements (expressing what the user does not want to retrieve). Both approaches use pairs of satisfaction degrees as the underlying framework but have different semantics, and thus also different operators for criteria evaluation, ranking, aggregation, etc

The Modeling of Interval-Valued Time Series Using Possibility Measure-Based Encoding-Decoding Mechanism

Interval-valued time series (ITS) is a collection of interval-valued data whose entires are ordered by time. The modeling of ITS is an ongoing issue pursued by many researchers. There are diverse ITS models showing better performance. This paper proposes a new ITS model using possibility measure-based encoding-decoding mechanism involved in fuzzy theory. The proposed model consists of four modules, say, linguistic variable generation module, encoding module, inference module and decoding module. The linguistic variable generation module can provide a series of linguistic variables expressed in fuzzy sets used to described dynamic characteristics of ITS. The encoding module encodes ITS into some embedding vectors with semantics with the aid of possibility measure and linguistic variables formed by linguistic variable generation module. The inference module uses artificial neural network to capture relationship implied in those embedding vectors with semantic. The decoding module decodes for the outputs of the inference module to produce the output of linguistic and interval formats by using the possibility measure-based encoding-decoding mechanism. In comparison with existing ITS models, the proposed model can not only produce the output of linguistic format, but also exhibit better numeric performance

Imprecise data fusion

Possibility theory offers a natural setting for representing imprecise data and poor information. This theory turns out to be quite useful for the purpose of pooling pieces of information stemming from several sources (for instance, several experts, sensors, or databases) . Indeed it looks more flexible than probability theory for the representation of aggregation modes that do not express averaging processes . This paper tentatively explains why possibility theory is appealing for the fusion of imprecise data, and it describes several aggregation modes it allows, along with their underlying assumptions . The existence of adaptive combination rules are pointed out, that take into account the level of conflict between the sources . This approach sounds natural in the pooling of expert opinions . It is suggested here that, under some assumptions, it might also be useful in sensor data fusion .La théorie des possibilités offre un cadre formel naturel pour la représentation de données imprécises, d'informations pauvres. Cette théorie prend tout son intérêt quand il s'agit d'agréger des informations issues de plusieurs sources (par exemple un groupe d'experts, un ensemble hétérogène de capteurs, plusieurs bases de données). En effet elle s'avère être beaucoup plus souple que la théorie des probabilités pour décrire des modes d'agrégation qui ne correspondent pas à des moyennes. Dans cet article on tente d'expliquer pourquoi la théorie des possibilités est intéressante dans le problème de fusion d'informations imprécises, et on décrit les modes d'agrégation qu'elle permet de représenter, avec les hypothèses qui les sous-tendent. On indique notamment l'existence d'opérations de combinaison adaptatives qui prennent en compte le niveau de conflit entre les sources. Cette approche semble justifiée pour l'agrégation d'opinions d'experts. On suggère ici qu'elle peut, dans certaines conditions, être utilisée pour la fusion multi-capteur

Default Theory : an Alternative Approach

Computer Scienc

Default reasoning and possibility theory

International audienceThis note discusses an approach, recently outlined by Ron Yager, to default reasoning based on possibility theory. Some limitations of his technique are pointed out, and remedied in the same theoretical framework. The proposed approach leads to address the question of fusing a default value with a piece of incomplete but certain information which may only partially contradict the default value