A methodology for acquiring qualitative knowledge for probabilistic graphical models

Abstract

We present a practical and general methodology that simplifies the task of acquiring and formulating qualitative knowledge for constructing probabilistic graphical models (PGMs). The methodology efficiently captures and communicates expert knowledge, and has significantly eased the model development process for three real-world problems in the domain of robotics.