Think Like An AI Agent: Introduction to Planning with LLMs

Understand how AI agents plan with the help of Generative AI

Large Language Models (LLMs) are typically seen as the “brain” behind autonomous AI because of the crucial role they are playing in the current emergence of autonomous agents. However, while much attention has been paid to LLMs' reasoning and tool-learning capabilities, their planning abilities—crucial for effective agent autonomy—have received less systematic analysis.

A new paper by Huang et al. provides a comprehensive taxonomy of planning approaches in LLM-based agents. In this deep dive, we'll analyze the paper's taxonomy of approaches, examining implementation details, and discussing the challenges that lie ahead for researchers in this rapidly evolving field.

If you want to learn how LLMs are reshaping agentic planning, stay awhile and listen.

Taxonomy on LLM-Agent planning. Source.

LLM Planning: The Fundamental Paradigm Shift

Planning—the ability to generate a sequence of actions to achieve a goal—has traditionally been the domain of symbolic AI systems or reinforcement learning approaches. The paper defines the general planning formulation as:

\(p = (a0, a1, ..., at) = plan(E, g; Θ, P)\)

Where p represents the plan (sequence of actions), E is the environment, g is the goal, Θ represents the LLM parameters, and P the prompt.

Traditional planning methods face significant limitations:

• Symbolic methods require conversion of natural language into formal representations

• These approaches lack error tolerance, failing with even minor errors

• Reinforcement learning methods require extensive interaction data

LLMs offer a promising alternative by leveraging their pre-trained knowledge and reasoning capabilities to approach planning in a more flexible and robust manner.

Huang et al. systematically categorize existing LLM-Agent planning approaches into five distinct but interconnected directions:

1. Task Decomposition

2. Multi-Plan Selection

3. External Planner-Aided Planning

4. Reflection and Refinement

5. Memory-Augmented Planning

Let's examine each approach in detail.