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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 those 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 NLP effectively improves the communication between human and intelligent agents. This article outlines the commons and relations between AI planning and NLP, and it argues that each of them can effectively impact the other one in six areas: (1) planning-based text understanding, (2) planning-based NLP, (3) text-based human\u2013robot interaction, (4) planning-based explainability, (5) evaluation metrics, and (6) applications. We also explore some potential future issues between AI planning and NLP. To the best of our knowledge, this survey is the first that addresses the deep connections between AI planning and NLP.<\/jats:p>","DOI":"10.1145\/3729236","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T11:09:36Z","timestamp":1744369776000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating AI Planning with Natural Language Processing:\u00a0A\u00a0Combination of Explicit and Tacit Knowledge"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2788-5756","authenticated-orcid":false,"given":"Kebing","family":"Jin","sequence":"first","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang,  China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-2578","authenticated-orcid":false,"given":"Hankz Hankui","family":"Zhuo","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology and School of Artificial Intelligence, Nanjing University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/SPICES.2017.8091339"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i7.16733"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17481"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.06.005"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10357"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-019-09425-x"},{"key":"e_1_3_2_8_2","volume-title":"5th International Conference on Learning Representations (ICLR \u201917)","author":"Bahdanau Dzmitry","year":"2017","unstructured":"Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. 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