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초록
영어
Context-awareness is the key element for building a smart home environment. The goal of a smart home is to predict the demand of home users and proactively provides the proper services by considering the user’s context information. Several methods are used in context-aware system to provide services. Machine learning based approaches are capable to make better prediction and adaptation than others. In this paper, we present machine learning based context-aware system which can provide service according to the trained model. Two effective learning algorithms: Back propagation Neural Network, and Temporal Differential (TD) class of reinforcement learning are used for prediction and adaptation respectively. This ap
목차
Abstract
1. Introduction
2. Proposed Method
2.1. Context Receiver Module
2.2. Service Selection Context Module
2.3. Service Selection Module
2.4. Service Provider Module
2.5. Service Execution Module
2.6. User Feedback Module
2.7. Adaptation Module
2.8. Knowledge Base
3. The Implementation Environment
4. Conclusion
References
1. Introduction
2. Proposed Method
2.1. Context Receiver Module
2.2. Service Selection Context Module
2.3. Service Selection Module
2.4. Service Provider Module
2.5. Service Execution Module
2.6. User Feedback Module
2.7. Adaptation Module
2.8. Knowledge Base
3. The Implementation Environment
4. Conclusion
References
키워드
- Back propagation Neural Network
- Context-awareness
- Smart home
- Temporal Difference class Reinforcement learning
저자정보
참고문헌
자료제공 : 네이버학술정보