Symbol Grounding and Task Learning from Imperfect Corrections
Mattias Appelgren | Alex Lascarides
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics
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 domain-level task in spite of the teacher’s mistakes.