This dissertation presents a new user interface architecture that treats user input as an uncertain process, approximates the probability distribution over possible interfaces using Monte Carlo sampling, and enables interface developers to easily build probabilistic user interfaces without needing to think probabilistically.

This architecture is embodied in the JULIA toolkit: a JavaScript User interface Library for tracking Interface Alternatives. To demonstrate the versatility and power of this architecture, the dissertation presents a collection of applications and interaction techniques built using the JULIA toolkit. This architecture provides the foundation for a new era of nondeterministic user interfaces that leverage probabilistic models to better infer user intent.

Understanding the Architecture

I've been told that Figure 1 provides the best overview of the approach in the toolkit. Next, I'd suggest watching my talk. Finally, my probabilistic feedback paper provides a concise architecture overview that I'd recommend reading.

If you have questions about the architecture, please don't hesitate to contact me.