A Multi-Agent System for Building Control
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
A semiotic multi-agent system for intelligent building control
Proceedings of the 1st international conference on Ambient media and systems
Advanced inference in situation-aware computing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Interoperable and adaptive fuzzy services for ambient intelligence applications
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Agents for energy efficiency in ubiquitous environments
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Learning patterns in ambient intelligence environments: a survey
Artificial Intelligence Review
Affect-aware behaviour modelling and control inside an intelligent environment
Pervasive and Mobile Computing
Distributing emotional services in Ambient Intelligence through cognitive agents
Service Oriented Computing and Applications
Discovering frequent user--environment interactions in intelligent environments
Personal and Ubiquitous Computing
Emerging and adaptive fuzzy logic based behaviours in activity sphere centred ambient ecologies
Pervasive and Mobile Computing
International Journal of Intelligent Information and Database Systems
Knowledge acquisition based on learning of maximal structure fuzzy rules
Knowledge-Based Systems
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Modern approaches to the architecture of living and working environments emphasize the dynamic reconfiguration of space and function to meet the needs, comfort, and preferences of its inhabitants. Although it is possible for a human operator to specify a configuration explicitly, the size, sophistication, and dynamic requirements of modern buildings demands that they have autonomous intelligence that could satisfy the needs of its inhabitants without human intervention. We describe a multiagent framework for such intelligent building control that is deployed in a commercial building equipped with sensors and effectors. Multiple agents control subparts of the environment using fuzzy rules that link sensors and effectors. The agents communicate with one another by asynchronous, interest-based messaging. They implement a novel unsupervised online real-time learning algorithm that constructs a fuzzy rule-base, derived from very sparse data in a nonstationary environment. We have developed methods for evaluating the performance of systems of this kind. Our results demonstrate that the framework and the learning algorithm significantly improve the performance of the building.