Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Adaptive Agent Architecture: Achieving Fault-Tolerance Using Persistent Broker Teams
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Foreground regions extraction and characterization towards real-time object tracking
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Kalman tracking with target feedback on adaptive background learning
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Information management for high performance autonomous intelligent systems
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Autonomous Agents and Multi-Agent Systems
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The emerging ubiquitous computing services integrate numerous distributed and heterogeneous components, which incur significantly high costs for their development, maintenance and administration. In this paper we introduce a middleware architecture for ubiquitous contextaware services which eases integration, while also including a wide range of features that maximize service autonomy. Autonomy is addressed at various levels, including context-acquisition components, situation modeling components and services. Several of these components are implemented as software agents given the advantages of agent technologies for realizing service autonomy. Along with these agents, adaptive perceptive interfaces ensuring autonomy at the context-acquisition level have been developed and integrated with the agent societies. The introduced architecture deals primarily with self-healing (recovery) and self-configuration (adaptation) characteristics of the typical autonomic systems. Following the illustration of the framework, we elaborate on how it has been used to support realistic prototype context-aware human centric and non-obtrusive services.