On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
(2401.02500)Abstract
Automated Planning and Scheduling is among the growing areas in 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.
Overview
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APS is a vital AI field for generating strategies or action sequences to meet goals, and LLMs are offering new advancements in this area.
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LLMs have progressed to perform complex text generation and are being incorporated into APS, blending language with traditional planning methods.
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The paper reviews 126 pieces of literature on LLM use in APS, finding they excel in simple contexts but haven't surpassed symbolic planners in complex planning.
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Future research should focus on improving LLM training for coherent, goal-oriented outputs and combining them with symbolic methods.
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LLMs have limitations but are a promising avenue in APS, potentially enabling more human-like reasoning in AI through creative and heuristic capabilities.
Introduction to LLMs and APS
Automated Planning and Scheduling (APS) is a valuable domain in AI, tasked with generating strategies or action sequences for achieving specific goals. Rooted in algorithms and system development, APS automates complex tasks, making systems more intelligent and adaptable. The rise of LLMs in AI, particularly within computational linguistics, has created an unprecedented opportunity to innovate in APS. The focus of the analyzed paper is on the intersection of these two areas, offering a new vantage point in how intelligently systems can plan and schedule tasks by harnessing natural language capabilities.
The Growth of LLMs in APS
LLMs have made significant strides, evolving from basic natural language processing tasks to complex, context-aware text generation. As they become more proficient, these models are increasingly incorporated into APS, using language constructs to define planning elements like preconditions and effects. By integrating traditional symbolic planners with the generative capacity of LLMs, systems can address complex planning challenges with both the creativity of human-like language processing and the accuracy of established planning methods.
Insights from the Literature Review
This paper takes an exhaustive look at recent literature—126 papers on LLMs' role in APS, categorized into eight applications of LLMs in APS: Language Translation, Plan Generation, Model Construction, Multi-agent Planning, Interactive Planning, Heuristics Optimization, Tool Integration, and Brain-Inspired Planning. Each category has been reviewed for the issues addressed and the gaps present. The literature suggests that while LLMs hold potential, their current application is absent of generating action sequences to rival symbolic planners. They shine in scenarios that aren't inherently complex, allowing them to speed up the plan generation process more efficiently than their symbolic counterparts.
Future Directions and Conclusion
The direction for future research is clear. Researchers are encouraged to pursue the development of LLM training methods to improve coherence and goal-oriented outputs and to explore neuro-symbolic integration following suggested taxonomies. Moreover, it's vital to create performance metrics for planners augmented by LLMs. In closing, while LLMs present challenges in their current form, they offer a promising frontier for planning and scheduling. Melding the creative and heuristic advantages of LLM with the exactitude of symbolic approaches stands to propel AI capabilities further into a realm that simulates more closely to human reasoning.
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