Automated planning focuses on strategies, building domain models and
synthesizing plans to transit initial states to goals. Natural language
processing concerns with the interactions between agents and human language,
especially processing and analyzing large amounts of natural language data.
These two fields have abilities to generate explicit knowledge, e.g.,
preconditions and effects of action models, and learn from tacit knowledge,
e.g., neural models, respectively. Integrating AI planning and natural language
processing effectively improves the communication between human and intelligent
agents. This paper outlines the commons and relations between AI planning and
natural language processing, argues that each of them can effectively impact on
the other one by four areas: (1) planning-based text understanding, (2)
planning-based text generation, (3) text-based human-robot interaction, and (4)
text-based explainable planning. We also explore some potential future issues
between AI planning and natural language processing.

本文介绍了人工智能规划和自然语言处理之间的相互作用，讨论了四个领域：基于规划的文本理解、基于规划的文本生成、基于文本的人机交互和基于文本的可解释规划，并探讨了一些潜在的未来问题。