How smart are our environments? An updated look at the state of the art
Pervasive and Mobile Computing
Artificial intelligence on the body, in the home, and beyond
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Review: Ambient intelligence: Technologies, applications, and opportunities
Pervasive and Mobile Computing
Annotating smart environment sensor data for activity learning
Technology and Health Care - Smart Environments: Technology to Support Healthcare
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multi-agent smart environments
Journal of Ambient Intelligence and Smart Environments
Inhabitant guidance of smart environments
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
Spatio-terminological inference for the design of ambient environments
COSIT'09 Proceedings of the 9th international conference on Spatial information theory
Predicting air quality in smart environments
Journal of Ambient Intelligence and Smart Environments
Exploring the responsibilities of single-inhabitant Smart Homes with Use Cases
Journal of Ambient Intelligence and Smart Environments
Learning automaton based on-line discovery and tracking of spatio-temporal event patterns
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
An adaptive sensor mining framework for pervasive computing applications
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Multi-agent smart environments
Journal of Ambient Intelligence and Smart Environments
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In this paper, we examine the problem of learning inhabitant behavioral models in intelligent environments. We maintain that inhabitant interactions in smart environments can be automated using a data-driven approach to generate hierarchical inhabitant models and learn decision policies. To validate this hypothesis, we have designed the ProPHeT decision-learning algorithm that learns a strategy for controlling a smart environment based on sensor observation, power line control, and the generated hierarchical model. The performance of the algorithm is evaluated using real data collected from our MavHome smart home and smart office environments.