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.

基于对话的人工智能协作可以在协作问题解决、创造性探索和社交支持方面起到革命性作用。本调查从传统的手工制作和信息状态方法到 AI 规划启发的方法，回顾了协同对话系统中对话管理范式的演变。然后，将焦点转向当代的数据驱动对话管理技术，这些技术旨在将深度学习在填充表格和开放领域环境中的成功经验转移到协作场景。本文还分析了一组将神经方法应用于协同对话管理的最新作品，突出了该领域的主要趋势。希望本调查为未来协同对话管理的发展提供基础背景，特别是在对话系统社区继续积极探索大型语言模型的潜力的情况下。