Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
The invisible future
A Middleware Infrastructure for Active Spaces
IEEE Pervasive Computing
Adaptive middleware for context-aware applications in smart-homes
MPAC '04 Proceedings of the 2nd workshop on Middleware for pervasive and ad-hoc computing
Service Adaptation Using Fuzzy Theory in Context-Aware Mobile Computing Middleware
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Ambient Intelligence, Wireless Networking, And Ubiquitous Computing
Ambient Intelligence, Wireless Networking, And Ubiquitous Computing
True Visions: The Emergence of Ambient Intelligence (Frontiers Collection)
True Visions: The Emergence of Ambient Intelligence (Frontiers Collection)
A Model of Sharing Based Multi-Agent to Support Adaptive Service in Ubiquitous Environment
ISA '08 Proceedings of the 2008 International Conference on Information Security and Assurance (isa 2008)
Proactive Fuzzy Control and Adaptation Methods for Smart Homes
IEEE Intelligent Systems
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Developing context-aware pervasive computing applications: Models and approach
Pervasive and Mobile Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Control and learning of ambience by an intelligent building
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A dynamic security framework for ambient intelligent systems: a smart-home based eHealth application
Transactions on computational science X
Genetic fuzzy markup language for game of NoGo
Knowledge-Based Systems
Information Sciences: an International Journal
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In Ambient Intelligence (AmI) vision, people should be able to seamlessly and unobtrusively use and configure the intelligent devices and systems in their ubiquitous computing environments without being cognitively and physically overloaded. In other words, the user should not have to program each device or connect them together to achieve the required functionality. However, although it is possible for a human operator to specify an active space configuration explicitly, the size, sophistication, and dynamic requirements of modern living environment demand that they have autonomous intelligence satisfying the needs of inhabitants without human intervention. This work presents a proposal for AmI fuzzy computing that exploits multiagent systems and fuzzy theory to realize a long-life learning strategy able to generate context-aware-based fuzzy services and actualize them through abstraction techniques in order to maximize the users' comfort and hardware interoperability level. Experimental results show that proposed approach is capable of anticipating user's requirements by automatically generating the most suitable collection of interoperable fuzzy services.