Learning Hierarchical Symbolic Representations to
Support Interactive Task Learning and Knowledge Transfer
Abstract
Interactive Task Learning (ITL) focuses on learning
the definition of tasks through online natural language instruction in real time. Learning the correct grounded meaning of the instructions is dif-
ficult due to ambiguous words, lack of common
ground, and the presence of distractors in the environment and the agent’s knowledge. We present
a learning strategy embodied in an ITL agent that
interactively learns in one shot the meaning of task
concepts for 40 games and puzzles in ambiguous
scenarios. Our approach learns hierarchical symbolic representations of task knowledge rather than
learning a mapping directly from perceptual representations. These representations enable the agent
to transfer and compose knowledge, analyze and
debug multiple interpretations, and communicate
efficiently with the teacher to resolve ambiguity.
We evaluate the efficiency of the learning by examining the number of words required to teach tasks
across cases of no transfer, positive transfer, and interference from prior tasks. Our results show that
the agent can correctly generalize, disambiguate,
and transfer concepts within variations in language
descriptions and world representations of the same
task, and across variations in different tasks