On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)


  • Vishal Pallagani University of South Carolina
  • Bharath Chandra Muppasani University of South Carolina
  • Kaushik Roy University of South Carolina
  • Francesco Fabiano New Mexico State University
  • Andrea Loreggia University of Brescia
  • Keerthiram Murugesan IBM Research
  • Biplav Srivastava University of South Carolina
  • Francesca Rossi IBM Research
  • Lior Horesh IBM Research
  • Amit Sheth University of South Carolina




Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners, pointing towards a promising neuro-symbolic approach. This approach effectively combines the generative aspects of LLMs with the precision of classical planning methods. By synthesizing insights from existing literature, we underline the potential of this integration to address complex planning challenges. Our goal is to encourage the ICAPS community to recognize the complementary strengths of LLMs and symbolic planners, advocating for a direction in automated planning that leverages these synergistic capabilities to develop more advanced and intelligent planning systems. We aim to keep the categorization of papers updated on https://ai4society.github.io/LLM-Planning-Viz/, a collaborative resource that allows researchers to contribute and add new literature to the categorization.




How to Cite

Pallagani, V., Muppasani, B. C., Roy, K., Fabiano, F., Loreggia, A., Murugesan, K., Srivastava, B., Rossi, F., Horesh, L., & Sheth, A. (2024). On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). Proceedings of the International Conference on Automated Planning and Scheduling, 34(1), 432-444. https://doi.org/10.1609/icaps.v34i1.31503