  Natural language processing (NLP) aims at investigating the interactions
between agents and humans, processing and analyzing large amounts of natural
language data. Large-scale language models play an important role in current
natural language processing. However, the challenges of explainability and
complexity come along with the developments of language models. One way is to
introduce logical relations and rules into natural language processing models,
such as making use of Automated Planning. Automated planning (AI planning)
focuses on building symbolic domain models and synthesizing plans to transit
initial states to goals based on domain models. Recently, there have been
plenty of works related to these two fields, which have the 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 five areas:
(1) planning-based text understanding, (2) planning-based natural language
processing, (3) planning-based explainability, (4) text-based human-robot
interaction, and (5) applications. We also explore some potential future issues
between AI planning and natural language processing. To the best of our
knowledge, this survey is the first work that addresses the deep connections
between AI planning and Natural language processing.
