A preference elicitation framework for automated planning

Sheryl Mantik, Minyi Li, Julie Porteous

Abstract: Highlights•We present a preference elicitation framework for automated planning.•It allows users to explore small and simple planning problems to elicit preferences.•It interacts with users through low burden comparison type queries.•It then learns from user query responses using machine learning techniques.•Our findings suggest that our framework can efficiently learn from user preferences.