Optimizing Interactive Systems via Data-Driven Objectives

Understand

Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.

• However, it is often challenging to find an objective to optimize for interactive systems (e.g., policy learning in task-oriented dialog systems).
• Generally, such objectives are manually crafted and rarely capture complex user needs in an accurate manner.
• We propose an approach that infers the objective directly from observed user interactions.