Meta-level amortization for user modeling and collaborative AI

This project aims to make modern cognitive user models and collaborative AI tools more applicable by developing generalizable amortization techniques for them.

In human-AI collaboration, one of the key difficulties is establishing a common ground for the interaction, especially in terms of goals and beliefs. In practice, the AI might not have access to this necessary information directly and must infer it during the interaction with the human. However, training a model to support this kind of inference would require massive collections of interaction data and is not feasible in most applications.
Modern cognitive models, on the other hand, can equip AI tools with the necessary prior knowledge to readily support inference, and hence, to quickly establish a common ground for collaboration with humans. However, utilizing these models in realistic applications is currently impractical due to their computational complexity and non-differentiable structure.
This micro-project contributes directly to the development of collaborative AI by making cognitive models practical and computationally feasible to use thus enabling efficient online grounding during interaction. The project approaches this problem by developing amortization techniques for modern cognitive models and for merging them in collaborative AI systems.

Output

A conference paper draft that introduces the problem, a method, and initial findings.

Project Partners

Primary Contact

Samuel Kaski, Delft University of Technology