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

arXiv: https://arxiv.org/abs/2401.02500 · HTML: https://mlsanity.com/paper/2401.02500/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 115 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 30 of 67)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://mlsanity.com/paper/2305.12295/) (2023)
- [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022)
- [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://mlsanity.com/paper/2310.01061/) (2023)
- [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://mlsanity.com/paper/2307.01928/) (2023)
- [Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training](https://mlsanity.com/paper/2309.17179/) (2023)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://mlsanity.com/paper/2305.11554/) (2023)
- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [OpenAGI: When LLM Meets Domain Experts](https://mlsanity.com/paper/2304.04370/) (2023)
- [AdaPlanner: Adaptive Planning from Feedback with Language Models](https://mlsanity.com/paper/2305.16653/) (2023)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Open-vocabulary Queryable Scene Representations for Real World Planning](https://mlsanity.com/paper/2209.09874/) (2022)
- [War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars](https://mlsanity.com/paper/2311.17227/) (2023)
- [Task and Motion Planning with Large Language Models for Object Rearrangement](https://mlsanity.com/paper/2303.06247/) (2023)
- [LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent](https://mlsanity.com/paper/2309.12311/) (2023)
- [RoboVQA: Multimodal Long-Horizon Reasoning for Robotics](https://mlsanity.com/paper/2311.00899/) (2023)
- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [Can Large Language Models Really Improve by Self-critiquing Their Own Plans?](https://mlsanity.com/paper/2310.08118/) (2023)

## Built on by (most cited first, 30 of 40)

- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces](https://mlsanity.com/paper/2410.09918/) (2024)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2404.03275/) (2024)
- [VLMPC: Vision-Language Model Predictive Control for Robotic Manipulation](https://mlsanity.com/paper/2407.09829/) (2024)
- [Octopus: On-device language model for function calling of software APIs](https://mlsanity.com/paper/2404.01549/) (2024)
- [Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis](https://mlsanity.com/paper/2502.20383/) (2025)
- [VeriPlan: Integrating Formal Verification and LLMs into End-User Planning](https://mlsanity.com/paper/2502.17898/) (2025)
- [Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction](https://mlsanity.com/paper/2402.15368/) (2024)
- [TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners](https://mlsanity.com/paper/2406.10196/) (2024)
- [LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics](https://mlsanity.com/paper/2504.21716/) (2025)
- [Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts](https://mlsanity.com/paper/2409.15915/) (2024)
- [ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees](https://mlsanity.com/paper/2504.21022/) (2025)
- [A Roadmap to Guide the Integration of LLMs in Hierarchical Planning](https://mlsanity.com/paper/2501.08068/) (2025)
- [Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery](https://mlsanity.com/paper/2505.16477/) (2025)
- [When Should Users Check? Modeling Confirmation Frequency in Multi-Step Agentic AI Tasks](https://mlsanity.com/paper/2510.05307/) (2025)
- [FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning](https://mlsanity.com/paper/2410.19727/) (2024)
- [Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs](https://mlsanity.com/paper/2409.00571/) (2024)
- [Vision-Language Model Predictive Control for Manipulation Planning and Trajectory Generation](https://mlsanity.com/paper/2504.05225/) (2025)
- [The Case for Developing a Foundation Model for Planning-like Tasks from Scratch](https://mlsanity.com/paper/2404.04540/) (2024)
- [LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space Exploration](https://mlsanity.com/paper/2405.01392/) (2024)
- [Neurosymbolic AI for Enhancing Instructability in Generative AI](https://mlsanity.com/paper/2407.18722/) (2024)
- [Knowledge Boundary and Persona Dynamic Shape A Better Social Media Agent](https://mlsanity.com/paper/2403.19275/) (2024)
- [Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM](https://mlsanity.com/paper/2505.08492/) (2025)
- [MuTRAP: Multi-trigger Trojans Attacking Robot Task Planning Systems](https://mlsanity.com/paper/2504.17070/) (2025)
- [Learning Hierarchical Domain Models Through Environment-Grounded Interaction](https://mlsanity.com/paper/2505.13497/) (2025)
- [NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models](https://mlsanity.com/paper/2510.09355/) (2025)
- [Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation](https://mlsanity.com/paper/2603.06064/) (2026)
- [Automating Transfer of Robot Task Plans using Functorial Data Migrations](https://mlsanity.com/paper/2406.15961/) (2024)
- [Model-Based AI planning and Execution Systems for Robotics](https://mlsanity.com/paper/2505.04493/) (2025)

## Abstract (arXiv)

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.