Navigating the Future: A Comprehensive Survey of Task Planning with Large Language Models

Introduction to Task Planning and LLMs

Task planning, a cornerstone of artificial intelligence, involves the intricate process of devising sequences of actions to achieve specific goals. This capability is fundamental for intelligent agents, requiring a deep understanding of their environment, robust logical reasoning, and effective sequential decision-making. Traditionally, task planning has relied on expert systems and manual configurations, approaches that often proved rigid and inefficient when faced with complex or dynamic scenarios. However, the advent of Large Language Models (LLMs) has ushered in a new era, significantly reshaping the landscape of task planning with their advanced reasoning and generalization capabilities.

This survey aims to provide a comprehensive and systematic overview of how LLMs are being employed and integrated into task planning methodologies. We will guide you through the theoretical foundations, explore the diverse range of current approaches, examine evaluation frameworks, and discuss the underlying mechanisms that empower LLMs in this domain. Our objective is to equip researchers and practitioners with a thorough understanding of the field, highlighting both its current state and its promising future directions.

Theoretical Foundations of Automated Planning

Before delving into LLM-specific strategies, it is crucial to establish a common understanding of automated planning. Automated planning deals with the representation of states, actions, and goals, and the search for a sequence of actions (a plan) that transforms an initial state into a goal state. Key definitions include:

• States: Descriptions of the environment at a particular point in time.
• Actions: Operations that can change the state of the environment. Actions typically have preconditions (conditions that must be true for the action to be applicable) and effects (changes to the state that result from the action).
• Goals: Desired states or conditions that the agent aims to achieve.
• Plans: Ordered sequences of actions that lead from an initial state to a goal state.

Automated planning can be categorized based on various criteria, such as the domain (e.g., classical planning, temporal planning, hierarchical planning) or the type of information available (e.g., fully observable, partially observable).

Taxonomy of LLM-Based Planning Methodologies

Contemporary research on LLM-based planning can be broadly categorized into three principal approaches, each leveraging LLMs in distinct ways to enhance planning capabilities:

1. External Module Augmented Methods

These methods focus on combining the powerful language understanding and generation capabilities of LLMs with external, specialized components that are adept at specific aspects of planning. This approach acknowledges that while LLMs excel at reasoning and understanding, they may benefit from the precision and efficiency of traditional planning algorithms or domain-specific tools.

• Integration with Classical Planners: LLMs can be used to translate natural language task descriptions into formal planning languages like the Planning Domain Definition Language (PDDL). PDDL provides a standardized way to represent planning problems, allowing classical AI planners to efficiently search for solutions. The LLM acts as an intelligent interface, bridging the gap between human-understandable requests and the formalisms required by traditional planners.
• Tool Use and API Integration: LLMs can be augmented with the ability to call external tools or APIs. This is particularly relevant for complex tasks that require interacting with the real world or specific software systems. The LLM can determine which tool is appropriate for a given subtask, formulate the necessary parameters, execute the tool, and interpret the results to continue the planning process. This allows LLMs to go beyond purely symbolic reasoning and engage with dynamic environments.
• Hybrid Systems: More sophisticated architectures combine LLMs with other AI techniques, such as reinforcement learning (RL) or knowledge graphs. For instance, an LLM might generate high-level plans, while an RL agent learns low-level control policies to execute those plans in a simulated or real environment. Knowledge graphs can provide structured background information to enrich the LLM