Generative AI Agent for Autonomous Match-3 Gameplay through Real-Time Image-Based Decision-Making

Abstract

Advances in artificial intelligence create more possibilities for autonomous gaming agents in video games. Therefore, this thesis focuses on building a generative AI agent for the Match-3 game. It combines Large Language Models (LLM) with Android Debug Bridge (ADB) commands. As a result, the agent can monitor the game screen in real-time, and then use specific prompts to enable the Large Language Model to analyze the image and execute appropriate actions. Unlike traditional AI agents that need pre-training or particular data, this method adapts to different Match-3 games with minimal setup. Next, we evaluated the agent across multiple games and found that it achieves performance levels similar to real players in certain aspects. The main contribution of this work is showing a new approach to agent development that uses LLMs for decision-making and ADB for action execution. This highlights the agent’s ability to adapt quickly to different games.