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