  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.
