Symbol Grounding and Task Learning from Imperfect Corrections

Abstract

This paper describes a method for learning from a teacher's potentially unreliable corrective feedback in an interactive task learning setting. The graphical model uses discourse coherence to jointly learn symbol grounding, domain concepts and valid plans. Our experiments show that the agent learns its domainlevel task in spite of the teacher's mistakes.