Cardiverse: Harnessing LLMs for Novel Card Game Prototyping

TL;DR

This paper presents Cardiverse, an automated framework leveraging LLMs for efficient card game prototyping, including novel mechanics, consistent environments, and scalable AI, significantly reducing human effort in game development.

Contribution

It introduces a graph-based variation generator, an LLM-driven game code system, and an ensemble-based AI construction method for card games.

Findings

Generated diverse novel game variations

Achieved consistent gameplay environment creation

Developed scalable AI with improved performance

Abstract

The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation. Recent advances in Large Language Models (LLMs) offer opportunities to automate and streamline these processes. However, it remains challenging for LLMs to design novel game mechanics beyond existing databases, generate consistent gameplay environments, and develop scalable gameplay AI for large-scale evaluations. This paper addresses these challenges by introducing a comprehensive automated card game prototyping framework. The approach highlights a graph-based indexing method for generating novel game variations, an LLM-driven system for consistent game code generation validated by gameplay records, and a gameplay AI constructing method that uses an ensemble of LLM-generated heuristic functions optimized through self-play. These contributions aim to accelerate card game prototyping, reduce human labor, and lower barriers to entry for game developers. For code repo visit this http URL https://github.com/danruili/Cardiverse