Dialogue-based human-AI collaboration can revolutionize collaborative problem-solving, creative exploration, and social support. To realize this goal, the development of automated agents proficient in skills such as negotiating, following instructions, establishing common ground, and progressing shared tasks is essential. This survey begins by reviewing the evolution of dialogue management paradigms in collaborative dialogue systems, from traditional handcrafted and information-state based methods to AI planning-inspired approaches. It then shifts focus to contemporary data-driven dialogue management techniques, which seek to transfer deep learning successes from form-filling and open-domain settings to collaborative contexts. The paper proceeds to analyze a selected set of recent works that apply neural approaches to collaborative dialogue management, spotlighting prevailing trends in the field. This survey hopes to provide foundational background for future advancements in collaborative dialogue management, particularly as the dialogue systems community continues to embrace the potential of large language models.

翻译：基于对话的人机协作能够革新协作式问题解决、创造性探索及社会支持。为实现这一目标，培养在谈判、遵循指令、建立共识及推进共享任务等方面表现优异的自动化智能体至关重要。本综述首先回顾协作对话系统中对话管理范式的演进历程，从传统手工构建与信息状态方法到基于AI规划的途径；随后转向当代数据驱动的对话管理技术——这些技术试图将深度学习的成功经验从表单填充及开放域场景迁移至协作语境。本文进而分析一批精选的近期研究，这些工作将神经方法应用于协作对话管理，突显该领域的主流趋势。本综述期望为协作对话管理的未来发展提供基础性背景，尤其是在对话系统社区持续拥抱大规模语言模型潜力的背景下。