Verbalization of Dependencies Between Concepts Built Through Fuzzy Cognitive Maps

The new computing paradigm known as cognitive computing attempts to imitate the human capabilities of learning, problem solving, and considering things in context. To do so, an application (a cognitive system) must learn from its environment (e.g., by interacting with various interfaces). These interfaces can run the gamut from sensors to humans to databases. Accessing data through such interfaces allows the system to conduct cognitive tasks that can support humans in decision-making or problem-solving processes. Cognitive systems can be integrated into various domains (e.g., medicine or insurance). For example, a cognitive system in cities can collect data, can learn from various data sources and can then attempt to connect these sources to provide real time optimizations of subsystems within the city (e.g., the transportation system). In this study, we provide a methodology for integrating a cognitive system that allows data to be verbalized, making the causalities and hypotheses generated from the cognitive system more understandable to humans. We abstract a city subsystem—passenger flow for a taxi company—by applying fuzzy cognitive maps (FCMs). FCMs can be used as a mathematical tool for modeling complex systems built by directed graphs with concepts (e.g., policies, events, and/or domains) as nodes and causalities as edges. As a verbalization technique we introduce the restriction-centered theory of reasoning (RCT). RCT addresses the imprecision inherent in language by introducing restrictions. Using this underlying combinatorial design, our approach can handle large data sets from complex systems and make the output understandable to humans.

Von Fuzzy-Sets zu Computing-with-Words

Dieser Artikel bietet einen Überblick über die Entwicklung und Zusammenhänge der einzelnen Elemente der Fuzzy-Logik, wovon Fuzzy-Set-Theorie die Grundlage bildet. Die Grundproblematik besteht in der Handhabung von linguistischen Informationen, die häufig durch Ungenauigkeit gekennzeichnet sind. Die verschiedenen technischen Anwendungen von Fuzzy-Logik bieten eine Möglichkeit, intelligentere Computersysteme zu konstruieren, die mit unpräzisen Informationen umgehen können. Solche Systeme sind Indizien für die Entstehung einer neuen Ära des Cognitive-Computing, di in diesemArtikel ebenfalls zur Sprache kommt. Für das bessere Verständnis wird der Artikel mit einem Beispiel aus der Meteorologie (d. h. Schnee in Adelboden) begleitet.

Synchronizing Mind Maps with Fuzzy Cognitive Maps for Decision-Finding in Cognitive Cities

Synchronizing mind maps with fuzzy cognitive maps can help to handle complex problems with many involved stakeholders by taking advantage of human creativity. The proposed approach has the capacity to instantiate cognitive cities by including cognitive computing. A use case in the context of decision-finding (concerning a transportation system) is presented to illustrate the approach.

Enhanced Knowledge Management by Synchronizing Mind Maps and Fuzzy Cognitive Maps

This paper presents a conceptual approach to enhance knowledge management by synchronizing mind maps and fuzzy cognitive maps. The use of mind maps allows taking advantage of human creativity, while the application of fuzzy cognitive maps enables to store information expressed in natural language. By applying cognitive computing, it makes possible to gather and extract relevant information out of a data pool. Therefore, this approach is supposed to give a framework that enhances knowledge management. To demonstrate the potential of this framework, a use case concerning the development of a smart city app is presented.

The influence of social connectedness and self-esteem on participant retention in a bi-ethnic longitudinal study

In prospective studies it is essential that the study sample accurately represents the target population for meaningful inferences to be drawn. Understanding why some individuals do not participate, or fail to continue to participate, in longitudinal studies can provide an empirical basis for the development of effective recruitment and retention strategies to improve response rates. This study examined the influence of social connectedness and self-esteem on long-term retention of participants, using secondary data from the “San Antonio Longitudinal Study of Aging” (SALSA), a population-based study of Mexican Americans (MAs) and European Americans (EAs) aged over 65 years residing in San Antonio, Texas. We tested the effect of social connectedness, self-esteem and socioeconomic status on participant retention in both ethnic groups. In MAs only, we analyzed whether acculturation and assimilation moderated these associations and/or had a direct effect on participant retention. ^ Low income, low frequency of social contacts and length of recruitment interval were significant predictors of non-completer status. Participants with low levels of social contacts were almost twice as likely as those with high levels of social contacts to be non-completers, even after adjustment for age, sex, ethnic group, education, household income, and recruitment interval (OR = 1.95, 95% CI: 1.26–3.01, p = 0.003). Recruitment interval consistently and strongly predicted non-completer status in all the models tested. Depending on the model, for each year beyond baseline there was a 25–33% greater likelihood of non-completion. The only significant interaction, or moderating, effect observed was between social contacts and cultural values among MAs. Specifically, MAs with both low social contacts and low acculturation on cultural values (i.e., placed high value on preserving Mexican cultural origins) were three and half times more likely to be non-completers compared with MAs in other subgroups comprised of the combination of these variables, even after adjustment for covariates. ^ Long term studies with older and minority participants are challenging for participant retention. Strategies can be designed to enhance retention by paying special attention to participants with low social contacts and, in MAs, participants with both low social contacts and low acculturation on cultural values. Minimizing the time interval between baseline and follow-up recruitment, and maintaining frequent contact with participants during this interval should also be is integral to the study design.^

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process

Adaptive fuzzy knowledge-based systems for control metabots' mobility on virtual environments

The confluence of three-dimensional (3D) virtual worlds with social networks imposes on software agents, in addition to conversational functions, the same behaviours as those common to human-driven avatars. In this paper, we explore the possibilities of the use of metabots (metaverse robots) with motion capabilities in complex virtual 3D worlds and we put forward a learning model based on the techniques used in evolutionary computation for optimizing the fuzzy controllers which will subsequently be used by metabots for moving around a virtual environment.

A Multi-Agent System for the Solution of Fuzzy Incidents in Telecommunications

This article presents a multi-agent expert system (SMAF) , that allows the input of incidents which occur in different elements of the telecommunications area. SMAF interacts with experts and general users, and each agent with all the agents? community, recording the incidents and their solutions in a knowledge base, without the analysis of their causes. The incidents are expressed using keywords taken from natural language (originally Spanish) and their main concepts are recorded with their severities as the users express them. Then, there is a search of the best solution for each incident, being helped by a human operator using a distancenotions between them.