Understanding the planning of LLM agents: A survey (2402.02716v1)

Abstract: As LLMs have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. We provide a taxonomy of existing works on LLM-Agent planning, which can be categorized into Task Decomposition, Plan Selection, External Module, Reflection and Memory. Comprehensive analyses are conducted for each direction, and further challenges for the field of research are discussed.

Understanding the Planning of LLM Agents: A Survey

The paper "Understanding the planning of LLM agents: A survey" provides a detailed analysis of the emerging role of LLMs in enhancing the planning capabilities of autonomous agents. As autonomous agents are recognized for their intelligent actions in various tasks, the integration of LLMs introduces novel methodologies for improving planning, a core aspect of these agents. The authors present a systematic view and taxonomy of existing works on LLM-based agent planning, providing valuable insights into task decomposition, plan selection, integration with external modules, reflection and refinement, and memory augmentation.

The survey outlines a clear taxonomy to categorize the diverse methodologies that have emerged in the field:

1. Task Decomposition: This strategy leverages LLMs to break down complex tasks into manageable sub-tasks, aligning with the divide and conquer principle. The paper distinguishes between decomposition-first methods, where the task is fully decomposed before execution, and interleaved decomposition methods, where decomposition and execution occur iteratively. This approach effectively enhances an agent's ability to handle complex environments by reducing cognitive load and preventing task forgetting.
2. Multi-Plan Selection: This methodology involves generating multiple plans and selecting the most optimal one using heuristic search algorithms. By harnessing the LLM's stochastic decoding capabilities, diverse plan trajectories can be sampled, enhancing overall planning efficacy. However, this approach imposes higher computational demands, raising questions about efficiency in resource-constrained environments.
3. External Planner-Aided Planning: The integration of LLMs with symbolic and neural planners overcomes the limitations of LLMs in handling complex constraints. Symbolic planners offer stability and interpretability, while neural planners bring efficiency and scalability. The combination of these systems is anticipated to address the shortcomings of traditional symbolic AI, enriching autonomous agents' planning strategies.
4. Reflection and Refinement: Reflection mechanisms in LLM agents foster resilience by enabling agents to reassess and refine plans in light of encountered errors. This approach enhances planning accuracy by integrating feedback loops, albeit at the cost of increased computational overhead due to iterative refinement cycles.
5. Memory-Augmented Planning: This approach capitalizes on RAG-based and embodied memory systems to enhance agents' planning capacities via retained experiences. While RAG-based memory offers real-time adaptability, embodied memory provides robust internalization of experiential knowledge, each with its trade-offs regarding update costs and retrieval accuracy.

The survey's experimental evaluation on benchmarks such as ALFWorld and ScienceWorld demonstrates the potential and challenges of each methodology, highlighting trade-offs between performance gains and computational expenses. Notably, methods such as Reflexion show considerable improvements in task success through planned reflection on errors.

The paper concludes by addressing key challenges in LLM-agent planning. Hallucinations and the feasibility and efficiency of generated plans remain significant hurdles. Moreover, the integration of multi-modal feedback and the development of fine-grained evaluation benchmarks are critical future directions for advancing the field. The combination of LLMs with traditional symbolic methodologies presents a promising path forward, suggesting a convergence of statistical and symbolic AI as a cornerstone for developing truly autonomous agents.

This survey makes a substantial contribution by mapping the landscape of LLM-agent planning, providing a framework for future work to build upon. The implications of this research extend beyond theoretical advancements, potentially catalyzing practical innovations in fields where autonomous agents operate, such as robotics, automated customer service, and smart systems. As research continues to address current limitations, LLMs are poised to redefine the capabilities of autonomous agents, offering substantial enhancements in decision-making and adaptability across diverse domains.