Combining LLM and GOAP: Building an Intelligent Goal-Oriented Action Planning System

This article explores combining large language models (LLM) with the traditional AI planning algorithm GOAP to build an intelligent system that can understand natural language goals and generate visual execution plans. The core innovation lies in using LLM's semantic understanding ability to process ambiguous natural language inputs, convert them into a format recognizable by the GOAP planner, generate optimal action sequences, and visualize them. This project provides reference value for the integration of traditional AI and large models.

GOAP is a classic planning algorithm in the fields of game AI and robotics, but it requires manual design of a large number of action rules and struggles to handle ambiguous natural language inputs. LLM has strong semantic understanding and reasoning capabilities, enabling it to easily understand human natural language goals. The combination of the two is expected to solve the pain points of traditional GOAP.

llm-goap-planner is an academic prototype developed by Aditya Khetawat and Aryan Thakur, built as a web application based on Spring Boot. The technical architecture is layered: the backend uses Spring Boot + Java17 to provide APIs; LLM is integrated locally via Ollama to ensure privacy; the GOAP module implements reverse planning; visualization uses Mermaid.js; the frontend is an HTML/CSS/JS interface.

System workflow: 1. The user inputs a natural language goal; 2. LLM parses and converts it into GOAP format; 3. The GOAP planner generates an action sequence; 4. Output JSON containing steps and Mermaid code; 5. The frontend renders the visual flow chart.

Application scenarios include: Game AI (NPC autonomous planning, understanding player instructions); enterprise process automation (lowering orchestration thresholds); project management (breaking down milestones into tasks); education and training (assisting in understanding AI planning principles).

Currently, it is an academic prototype and not for production deployment. Limitations: Based on the GOAP algorithm, complex scenarios require efficient search; LLM output has randomness; the action library relies on manual maintenance. Outlook: It provides a reference for the integration of traditional AI and large models, and LLM can serve as a semantic interface.

The project demonstrates the possibility of collaboration between traditional AI and large models: LLM is responsible for understanding and communication, while traditional AI handles planning and execution. For developers, it is a learning example of combining classic algorithms with cutting-edge technology.