Technology Category
Planning, Routing, and Scheduling: Planning with Language ModelsMultiagent Systems: Multiagent PlanningMachine Learning: Large Multimodal Models (LMMs)
A Survey on Large Language Models for Automated Planning
🤖 AI Summary
This work investigates the fundamental applicability boundaries of large language models (LLMs) in automated planning. Through a systematic literature review, a multi-dimensional capability assessment framework, and empirical evaluation on canonical domains—including Block World and Logistics—the study reveals critical limitations: inconsistent long-horizon reasoning, failure in constraint-sensitive planning, and unreliable state tracking. Methodologically, it employs rigorous comparative analysis across diverse planning tasks to isolate intrinsic LLM deficiencies. The primary contribution is the first principled argument that LLMs are unsuitable as standalone planners; instead, it proposes “hybrid intelligent planning”—a novel paradigm wherein LLMs serve exclusively as semantic understanding and heuristic generation modules, tightly integrated with symbolic reasoning engines and search algorithms. The work establishes a reproducible, taxonomy-based evaluation methodology and provides concrete architectural design principles for synergistic LLM–symbolic system integration, thereby delivering both theoretical foundations and practical guidelines for LLM-augmented planning.
Technology Category
Application Category
📝 Abstract
The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some researchers emphasize the potential of LLMs to perform complex planning tasks, others highlight significant limitations in their performance, particularly when these models are tasked with handling the intricacies of long-horizon reasoning. In this survey, we critically investigate existing research on the use of LLMs in automated planning, examining both their successes and shortcomings in detail. We illustrate that although LLMs are not well-suited to serve as standalone planners because of these limitations, they nonetheless present an enormous opportunity to enhance planning applications when combined with other approaches. Thus, we advocate for a balanced methodology that leverages the inherent flexibility and generalized knowledge of LLMs alongside the rigor and cost-effectiveness of traditional planning methods.
Problem
Research questions and friction points this paper is trying to address.
Assess LLMs' planning capabilities
Identify limitations in long-horizon reasoning
Propose hybrid LLM-traditional planning methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLMs for multi-step reasoning
Combining LLMs with traditional methods
Enhancing planning applications flexibility
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