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.

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The future digital battlespace will be a fast-paced and frenetic environment that stresses information communication technology systems to the limit. The challenges are most acute in the tactical and operational domains where bandwidth is severely limited, security of information is paramount, the network is under physical and cyber attack and administrative support is minimal. Hyperion is a cluster of research projects designed to provide an automated and adaptive information management capability embedded in defence networks. The overall system architecture is designed to improve the situational awareness of field commanders by providing the ability to fuse and compose information services in real time. The key technologies adopted to enable this include: autonomous software agents, self-organizing middleware, a smart data filtering system and a 3-D battlespace simulation environment. This paper reviews some of the specific techniques under development within the Hyperion sub-projects and the results achieved to date.

The paper introduces a hybrid evolving architecture for dealing with incremental learning. It consists of two components: resource allocating neural network (RAN) and growing Gaussian mixture model (GGMM). The architecture is motivated by incrementality on one hand and on the other hand by the possibility to handle unlabeled data along with the labeled one, given that the architecture is dedicated to classification problems. The empirical evaluation shows the efficiency of the proposed hybrid learning architecture.