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Fuzzy Logic Controllers (FLCs) represent one of the most successful methodologies exploiting fuzzy logic to model complex systems characterized by ambiguity and vagueness. Fuzzy logic applied in the control theory allows to reduce the complexity of the FLC design process thanks to the linguistic representation of the systems' behaviour. However, in spite of their benefit, the implementation of FLCs is affected by a significant drawback, i.e., the strong dependence on hardware architecture. In order to overcome this limitation, an XML-based language, named Fuzzy Markup Language (FML), has been introduced. FML allows designers to model FLCs in a human-readable and hardware-independent way. FML benefits arise from the exploitation of an alternative representation of a FLC based on labeled trees, data structure derived from XML-based document representation. However, this new graphical FLC representation can be exploited to implement an enhanced visual environment which allows designers to easily model a FLC through visual steps. This chapter is devoted, firstly, to present the new graphical representation of a FLC based on labeled trees, and, secondly, to describe the implemented framework, named Visual FML Tool, capable of exploiting labeled tree benefits by achieving a twofold purpose: the simplification of the FLC design through simple visual steps and a hardware-independent FLC modeling thanks to the direct mapping of the FLC labeled tree in a FML program. © 2013 Springer-Verlag Berlin Heidelberg.

Fuzzy Markup Language (FML) is a specific-purpose computer language which is emerging as a standard tool for modeling Fuzzy Logic Controllers (FLCs) in a human-readable and hardware-independent way. By means of these features FML allows FLCs designers to define their systems independently from their legacy representations and provides them with a collection of facilities speeding up the whole development process of a fuzzy system. However, besides the hardware independence feature, the XML nature of FMLmakes possible to embed fuzzy reasoning in computing scenarios characterized by high levels of pervasiveness and ubiquity. Through this additional feature, FML technology may efficiently distribute pieces of the global control flow on different devices populating a pervasive environment. In this chapter, FML features are exploited to implement an agent-based framework designed for providing proactive and distributed services in an Ambient Intelligent (AmI) scenario. In particular, FML is combined with a distributed learning strategy for capturing habits of users that live a smart environment and generating an appropriate collection of services satisfying users' preferences.As will be shown by a case study, the exploitation of FML distributed features simultaneously allows to achieve the relevant advantage of reducing the fuzzy computation effort and providing the most suitable collection of distributed services fulfilling a prefixed objective. © 2013 Springer-Verlag Berlin Heidelberg.

Ambient Intelligence (AmI) 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 satisfying 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, thanks to a hybrid approach based on Computational Intelligence methodologies and standard Semantic Web technologies, we will describe how ubiquitous devices are able to find the suitable set of 'intelligent' services in a transparent way. © 2007 Springer-Verlag Berlin Heidelberg.

The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. Heart rate variability (HRV) is a physiological phenomenon where the time interval between heart beats varies. It is measured by the variation in the beat-to-beat interval. Based on the HRV analysis, the medical experts can make a health assessment for our body, especially for the heart. This chapter presents a fuzzy markup language (FML)-based HRV ontology to apply to ECG domain knowledge. The ontology technologies are used to construct the personalized HRV ontology, and the FML is used to describe the knowledge base and rule base of the HRV. The ECG signals were collected from the involved students before they took exams and after they finished the exams. Based on the constructed FML-based HRV ontology, the variation in the nervous level between before exams and after exams could be inferred when implementing the proposed approach. An experimental platform has been constructed to test the performance of the ontology. The results indicate that the proposed method can work feasibly. © 2012 Springer-Verlag London Limited.

Pervasive and human-centric computing is beginning to be fact: with cell phones, laptops and handhelds, human beings can work pretty much anywhere. Ambient Intelligence (AmI) is a novel human-centric computer discipline based on three emergent technologies: Ubiquitous Computing, Ubiquitous Communication and Intelligent User Interfaces. The integration of aforesaid technologies opens new scenarios for improving the interaction between humans and information technology equipments realizing a human-centric computing environment. Within this aim the deliverable of tasks or services should be achieved through the usage of an invisible network of heterogeneous devices composing dynamic computational-ecosystems capable of satisfying the users requirements. Fuzzy granules, intended as a clump of objects which are drawn together by criteria like indistinguishability, similarity, proximity or functionality, can represent a powerful and, simultaneously, simple paradigm to embed intelligence into a generic AmI ecosystem in order to support people in carrying out their everyday life activities, tasks and rituals in easy and natural way. However, the strong dinamicity and the high complexity characterizing a typical AmI scenario make difficult and expensive to design ad-hoc fuzzy granules. This paper presents a framework exploiting methodologies coming from Semantic Web and Computational Intelligence areas to compose fuzzy granules in autonomous way in order to maximize the users comfort and achieve the hardware transparency and interoperability. © 2009 Springer-Verlag Berlin Heidelberg.

Ambient Intelligence (AmI, shortly) gathers best re-sults from three key technologies, Ubiquitous Computing, Ubiq-uitous Communication, and Intelligent User Friendly Inter-faces. The functional and spatial distribution of tasks is a natu-ral thrust to employ multi-agent paradigm to design and imple-ment AmI environments. Two critical issues, common in most of applications, are (1) how to detect in a general and efficient way context from sensors and (2) how to process contextual in-formation in order to improve the functionality of services. In this work we experiment a framework where hybrid techniques (distributed fuzzy control, mobile agents, fuzzy rules induction algorithms) are mixed to gain flexibility and uniformity.