Towards a cognitive architecture to enable natural language interaction in co-constructive task learning

2025-03-31

AI Summary

This study addresses the human–AI collaborative requirements in Collaborative Constructive Task Learning (CCTL), where natural language serves as the primary interaction modality. Method: We propose the first cross-domain unified cognitive architecture specifically designed for CCTL, systematically integrating human memory mechanisms—namely, semantic and working memory modeling—with Interactive Task Learning (ITL) paradigms and multimodal language theory. The approach unifies cognitive modeling, computational linguistics, and human–computer interaction techniques. Contribution/Results: We formally specify the core cognitive capabilities essential for CCTL, delineate key technical challenges, and identify concrete implementation pathways. The resulting architecture enables natural-language-driven task understanding, dynamic memory integration, and multimodal interaction. It establishes a scalable theoretical foundation and practical technical framework for natural-language-native agent learning, advancing principled, cognition-informed AI design for collaborative task environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This research addresses the question, which characteristics a cognitive architecture must have to leverage the benefits of natural language in Co-Constructive Task Learning (CCTL). To provide context, we first discuss Interactive Task Learning (ITL), the mechanisms of the human memory system, and the significance of natural language and multi-modality. Next, we examine the current state of cognitive architectures, analyzing their capabilities to inform a concept of CCTL grounded in multiple sources. We then integrate insights from various research domains to develop a unified framework. Finally, we conclude by identifying the remaining challenges and requirements necessary to achieve CCTL in Human-Robot Interaction (HRI).

Problem

• Identify cognitive architecture traits for natural language in CCTL
• Analyze current architectures to inform a multi-source CCTL concept
• Develop unified framework integrating insights from diverse research domains

Innovation

• Cognitive architecture for natural language interaction
• Multi-modality integration in task learning
• Unified framework from diverse research insights