Giovanni Acampora

Fuzzy control interoperability and scalability for adaptive domotic framework

The evolution of the microprocessor industry, combined with the reduction on cost and increase of efficiency, gives rise to new scenario for ubiquitous computing where humans trigger seamlessly activities and tasks using unusual (often imperceptible) interfaces according to physical space and context. Many problems must be faced: adaptivity, hybrid control strategies, system (hardware) integration, and ubiquitous networking access. In this paper, a solution that attempts to provide a flexible and dependable solution to these complicated problems is illustrated. First, an extensible markup language (XML)-derived technologies is proposed to define fuzzy markup language (FML), a markup language skilled for defining detailed structure of fuzzy control independent from its legacy representation. FML is essentially composed of three layers: 1) XML in order to create a new markup language for fuzzy logic control; 2) document type definition in order to define the legal building blocks; and 3) extensible stylesheet language transformations in order to convert a fuzzy controller description into a specific programming language. Then an agent-based framework designed for providing proactive services in domotic environments, is presented. The agent architecture, exploiting mobile computation, is able to maximize the fuzzy control deployment for the natively FML representation by performing an efficient distribution of pieces of the global control flow over the different computers. Agents are also used to capture user habits, to identify requests, and to apply the artefact-mediated activity through an adaptive fuzzy control strategy. The architecture adopts interoperability techniques that, combined with sophisticated control facilities, represent an efficient experience for adaptive domotic framework.

A proposal of ubiquitous fuzzy computing for Ambient Intelligence

Ambient Intelligence is considered as the composition of three emergent technologies: Ubiquitous Computing, Ubiquitous Communication and Intelligent User Interfaces. The aim of integration of aforesaid technologies is to make wider the interaction between human beings and information technology equipment through the usage of an invisible network of ubiquitous computing devices composing dynamic computational-ecosystems capable of satisfy the users requirements. Many works focus the attention on the interaction from users to devices in order to allow an universal and immediate access to available content and services provided by the environment. This paper, vice versa, focuses on the reverse interactions, from devices to users, in order to realize a collection of autonomous control services able to minimize the human effort. In particular, by merging computational intelligence methodologies with standard Web technologies we show how ubiquitous devices will be able to find the suitable set of intelligent services in a transparent way.

A hybrid evolutionary approach for solving the ontology alignment problem

Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so‐called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high‐quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms.

Ontology-based multi-agents for intelligent healthcare applications

A healthy diet and lifestyle are the most effective approaches to prevent disease. Good eating habits are central to a healthy lifestyle. When a person eats too much or too little on a continual basis, the risk of disease will increase. Therefore, developing healthy and balanced eating habits is essential to disease prevention. This paper proposes an ontology-based multi-agents (OMAS), including a personal knowledge agent, a fuzzy inference agent, and a semantic generation agent, for evaluating the health of diets. Using the proposed approach, domain experts can create nutritional facts for common Taiwanese foods. Next, the users are requested to input foods eaten. Finally, the food ontology and personal profile ontology are constructed by domain experts. Fuzzy markup language (FML) is used to describe the knowledge base and rule base of the OMAS. Additionally, web ontology language (OWL) is employed to describe the food ontology and personal profile ontology. Finally, the OMAS semantically analyzes dietary status for users based on the pre-constructed ontology and fuzzy inference results. Using the generated semantic analysis, people can obtain health information about what they eat, which can lead to a healthy lifestyle and healthy diet. Experimental results show that the proposed approach works effectively and diet health status can be provided as a reference to promote healthy living.

On the Temporal Granularity in Fuzzy Cognitive Maps

The theory of fuzzy cognitive maps (FCMs) is a powerful approach to modeling human knowledge that is based on causal reasoning. Taking advantage of fuzzy logic and cognitive map theories, FCMs enable system designers to model complex frameworks by defining degrees of causality between causal objects. They can be used to model and represent the behavior of simple and complex systems by capturing and emulating the human being to describe and present systems in terms of tolerance, imprecision, and granulation of information. However, FCMs lack the temporal concept that is crucial in many real-world applications, and they do not offer formal mechanisms to verify the behavior of systems being represented, which limit conventional FCMs in knowledge representation. In this paper, we present an extension to FCMs by exploiting a theory from formal languages, namely, the timed automata, which bridges the aforementioned inadequacies. Indeed, the theory of timed automata enables FCMs to effectively deal with a double-layered temporal granularity, extending the standard idea of B-time that characterizes the iterative nature of a cognitive inference engine and offering model checking techniques to test the cognitive and dynamic comportment of the framework being designed.

On the Power of Fuzzy Markup Language

One of the most successful methodology that arose from the worldwide diffusion of Fuzzy Logic is Fuzzy Control. After the first attempts dated in the seventies, this methodology has been widely exploited for controlling many industrial components and systems. At the same time, and very independently from Fuzzy Logic or Fuzzy Control, the birth of the Web has impacted upon almost all aspects of computing discipline. Evolution of Web, Web2.0 and Web 3.0 has been making scenarios of ubiquitous computing much more feasible; consequently information technology has been thoroughly integrated into everyday objects and activities. What happens when Fuzzy Logic meets Web technology? Interesting results might come out, as you will discover in this book. Fuzzy Mark-up Language is a son of this synergistic view, where some technological issues of Web are re-interpreted taking into account the transparent notion of Fuzzy Control, as discussed here. The concept of a Fuzzy Control that is conceived and modeled in terms of a native web wisdom represents another step towards the last picture of Pervasive Web Intelligence.

Ontology-based intelligent fuzzy agent for diabetes application

It is widely pointed out that classical ontologies are not sufficient to deal with imprecise and vague knowledge for some real world applications, but the fuzzy ontology can effectively solve data and knowledge with uncertainty. In this paper, an ontology-based intelligent fuzzy agent (OIFA), including a fuzzy markup language (FML) generating mechanism, a FML parser, a fuzzy inference mechanism, and a semantic decision making mechanism, is proposed to apply to the semantic decision making for diabetes domain. In addition, a FML-based definition is considered modeling the knowledge base and rule base of the fuzzy objects and inference operators. The experimental results show that the proposed method is feasible for diabetes semantic decision-making.

Distributing emotional services in Ambient Intelligence through cognitive agents

Ambient Intelligence (AmI) is a pervasive computing paradigm whose main aim is to design smart environments composed of invisible, connected, intelligent and interactive systems, which are naturally sensitive and responsive to the presence of people, providing advanced services for improving the quality of life. Nevertheless, AmI systems are more than a simple integration among computer technologies; indeed, their design can strongly depend upon psychology and social sciences aspects describing, analysing and forecasting the human being status during the system’s decision making. This paper introduces a novel methodology for AmI systems designing that exploits a service-oriented architecture whose functionalities are performed by a collection of so-called cognitive agents. These agents exploit a novel extension of Fuzzy Cognitive Maps benefiting on the theory of Timed Automata and a formal method for representing human moods in order to distribute emotional services able to enhance users’ comfort and simplify the human/systems interactions. As will be shown in experimental results, where a usability study and a confirmation of expectations test have been performed, the proposed approach maximizes the system’s usability in terms of efficiency, accuracy and emotional response.

Combining multi agent paradigm and memetic computing for personalized and adaptive learning experiences

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. Computational Intelligence methodologies can support e‐Learning system designers in two different aspects: (1) they represent the most suitable solution able to support learning content and activities, personalized to specific needs and influenced by specific preferences of the learner and (2) they assist designers with computationally efficient methods to develop “in time” e‐Learning environments. This article attempts to achieve both results by exploiting an ontological representations of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework. This synergy enables multi‐island memetic approach managing a collection of models and processes for adapting an e‐Learning system to the learner expectations and to formulate objectives in an effective and dynamic intelligent way. More precisely, our proposal exploits ontological representations of learning environment and a memetic distributed problem‐solving approach to generate the best learning presentation and, at the same time, minimize the computational efforts necessary to compute optimal learning experiences.

Optimizing learning path selection through memetic algorithms

e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way.