Machine Learning
CT '97 Proceedings of the 2nd International Conference on Cognitive Technology (CT '97)
Designing Smart Homes: The Role of Artificial Intelligence (Lecture Notes in Computer Science)
Designing Smart Homes: The Role of Artificial Intelligence (Lecture Notes in Computer Science)
The WM method completed: a flexible fuzzy system approach to data mining
IEEE Transactions on Fuzzy Systems
Multi-agent software control system with hybrid intelligence for ubiquitous intelligent environments
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Efficient category-based service discovery on multi-agent platform
Information Systems Frontiers
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In this paper, a novel ambient intelligence (AmI) platform is proposed to facilitate fast integration of different control algorithms, device networks and user interfaces. This platform defines the overall hardware/software architecture and communication standards. It consists of four layers, namely the ubiquitous environment, middleware, multi-agent system and application layer. The multi-agent system is implemented using Java Agent DEvelopment (JADE) framework and allows users to incorporate multiple control algorithms as agents for managing different tasks. The Universal Plug and Play (UPnP) device discovery protocol is used as a middleware, which isolates the multi-agent system and physical ubiquitous environment while providing a standard communication channel between the two. An XML content language has been designed to provide standard communication between various user interfaces and the multi-agent system. A mobile ubiquitous setup box is designed to allow fast construction of ubiquitous environments in any physical space. The real time performance analysis shows the potential of the proposed AmI platform to be used in real-life AmI applications. A case study has also been carried out to demonstrate the possibility of integrating multiple control algorithms in the multi-agent system and achieving a significant improvement on the overall offline learning performance.