Improving Interface Design in Interactive Task Learning for Hierarchical Tasks based on a Qualitative Study

TL;DR

This paper investigates interface design improvements for Interactive Task Learning systems handling hierarchical tasks, using qualitative analysis of user interactions and proposing a new Editable Hierarchy Knowledge interface.

Contribution

It provides novel insights into dialogue language, instruction strategies, and error handling, and introduces the EHK interface for better hierarchical task learning.

Findings

Insights on dialogue language types and instruction strategies

Design principles for error handling in ITL systems

Proposal of the Editable Hierarchy Knowledge interface

Abstract

Interactive Task Learning (ITL) systems acquire task knowledge from human instructions in natural language interaction. The interaction design of ITL agents for hierarchical tasks stays uncharted. This paper studied Verbal Apprentice Learner(VAL) for gaming, as an ITL example, and qualitatively analyzed the user study data to provide design insights on dialogue language types, task instruction strategies, and error handling. We then proposed an interface design: Editable Hierarchy Knowledge (EHK), as a generic probe for ITL systems for hierarchical tasks.