Machine Learning
Distributed mediation of ambiguous context in aware environments
Proceedings of the 15th annual ACM symposium on User interface software and technology
Applications of context-aware computing in hospital work: examples and design principles
Proceedings of the 2004 ACM symposium on Applied computing
Reasoning about Uncertain Contexts in Pervasive Computing Environments
IEEE Pervasive Computing
IEEE Pervasive Computing
Human-Computer Interaction
Context Awareness and Uncertainty in Collocated Collaborative Systems
Groupware: Design, Implementation, and Use
Advanced inference in situation-aware computing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Advanced fuzzy inference engines in situation aware computing
Fuzzy Sets and Systems
Activity recognition using temporal evidence theory
Journal of Ambient Intelligence and Smart Environments
Uncertainty Management in Context-Aware Applications: Increasing Usability and User Trust
Wireless Personal Communications: An International Journal
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In context-aware systems, one of the main challenges is how to tackle context uncertainty well, since perceived context always yields uncertainty and ambiguity with consequential effect on the performance of context-aware systems. We argue that uncertainty is mainly generated by two sources. One is sensor's inherent inaccuracy and unreliability. The other source is deduction process from low-level context to high-level context. Decision tree is an appropriate candidate for reasoning. Its distinct merit is that once a decision tree has been constructed, it is simple to convert it into a set of human-understandable rules. So human can easily improve these rules. However, one inherent disadvantage of decision tree is that the use of crisp points makes the decision trees sensitive to noise. To overcome this problem, we propose an alternative method, fuzzy decision tree, based on fuzzy set theory.