A mbient intelligence is an exciting new information technology paradigm in which people are empowered through a digital environment that is aware of their presence and context and is sensitive, adaptive, and responsive to their needs. 1 Ambient-intelligence environments are characterized by their ubiquity, transparency, and intelligence. In these environments, a multitude of interconnected, invisible embedded systems, seamlessly integrated into the background, surround the user. The system recognizes the people that live in it and programs itself to meet their needs by learning from their behavior. 1 To realize the ambient-intelligence vision, people must be able to seamlessly and unobtrusively use and configure the computer-based artifacts and systems in their ubiquitous-computing environments without being cognitively overloaded. 1 The user shouldn't have to program each device or connect them together to achieve the required functionality. The complexity associated with the number, varieties, and uses of computer-based artifacts requires that we design a system that lets intelligence disappear into the infrastructure of active spaces (such as buildings, shopping malls, theaters, and homes), 2 automatically learning to carry out everyday tasks based on the users' habitual behavior.Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambientintelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.
We describe a new approach to intelligent building systems, that utilises an intelligent agent approach to autonomously governing the building environment. We discuss the role of learning in building control systems, and contrast this approach with existing IB solutions. We explain the importance of acquiring information from sensors, rather than relying on pre-programmed models, to determine user needs. We describe how our architecture, consisting of distributed embedded agents, utilises sensory information to learn to perform tasks related to user comfort, energy conservation, safety and monitoring functions. We show how these agents, employing a behaviour-based approach derived from robotics research, are able to continuously learn and adapt to individuals within a building, while always providing a fast, safe response to any situation. Finally, we show how such a system could be used to provide support for older people, or people with disabilities, allowing them greater independence and quality of life.
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