Application of autonomic agents for global information grid management and security
Proceedings of the 2007 Summer Computer Simulation Conference
PlaceSense: a tool for sensing communities
ISWPC'09 Proceedings of the 4th international conference on Wireless pervasive computing
Adapting stereotypes to handle dynamic user profiles in a pervasive system
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
A Personal Smart Space approach to realising Ambient Ecologies
Pervasive and Mobile Computing
Learning user preferences for adaptive pervasive environments: An incremental and temporal approach
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Dynamic context-aware personalisation in a pervasive environment
Pervasive and Mobile Computing
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If current trends in cellular phone technology, personal digital assistants, and wireless networking are indicative of the future, we can expect our environments to contain an abundance of networked computational devices and resources. We envision these devices acting in an orchestrated manner to meet usersý needs, pushing the level of interaction away from particular devices and towards interactions with the environment as a whole. Computation will be based not only on input explicitly provided by the user, but also on contextual information passively collected by networked sensing devices. Configuring the desired responses to different situations will need to be easy for users. However, we anticipate that the triggering situations for many desired automation policies will be complex, unforeseen functions of low-level contextual information. This is problematic since users, though easily able to perceive triggering situations, will not be able to define them as functions of the devicesý available contextual information, even when such a function (or a close approximation) does exist. In this paper, we present an alternative approach for specifying the automation rules of a pervasive computing environment using machine learning techniques. Using this approach, users generate training data for an automation policy through demonstration, and, after training is completed, a learned function is employed for future automation. This approach enables users to automate the environment based on changes in the environment that are complex, unforeseen combinations of contextual information. We developed our learning service within Gaia, our pervasive computing system, and deployed it within our prototype pervasive computing environment. Using the system, we were able to have users demonstrate how sound and lighting controls should adjust to different applications used within the environment, the users present, and the locations of those users and then automate those demonstrated preferences.