Huadong Wu ’ s dissertation contribution

1. Huadong Wu’s dissertation contribution • Introducing Dempster-Shafer theory to context-aware computing • It is suitable to combine objective data with subjective judgments • Add uncertainty management – able to handle ambiguity & ignorance in probability • The reasoning process accounts all evidence as an ensemble – it can handle nested hypotheses that cannot be handled by the classical Bayesian methods • when hypotheses are mutually exclusive, the canonical Bayesian method emerges clearly as a subset of DS  bottom-line: DS is as good as Bayesian-based methods • Extending Dempster-Shafer theory • Easily realize differential trust scheme on sensors, which cannot easily be handled in traditional sensor fusion methods, and it provides a easy way for human intervention • Mitigate conflicts that cause counter-intuitive (to somebody) results using the classic Dempster-Shafer evidence combination rule • Incorporate sensors’ behavior evolution (drift) information, thus outperform traditional methods that are static • System-building methodology and context-sensing architecture • Context consolidated, context-requiring applications are further separated from context-sensing implementation • Sensor observation joint distribution not required (DS-related) • Robust to change in sensor set and sensors’ characteristics • The system is easily scalable to add new sensor fusion or AI algorithms