构建支持自然语言交互的协同任务学习认知架构
Towards a cognitive architecture to enable natural language interaction in co-constructive task learning
- 融合人类记忆机制与多模态交互，设计可理解语言的机器人认知框架
- 提出统一框架，使机器人能通过对话协同完成任务
- 适合人机协作、智能助理等需自然语言交互的场景
本研究探讨在协同构建任务学习（CCTL）中，认知架构需具备哪些特性才能有效利用自然语言的优势。首先回顾交互式任务学习（ITL）、人类记忆系统机制，以及自然语言与多模态交互的重要性。接着分析现有认知架构的能力，整合多领域研究成果，提出一个基于多重来源的CCTL概念框架。最后指出实现人机交互中CCTL仍面临的挑战与必要条件。
原文摘要 · Abstract (English)
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).
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