SpellForger: Prompting Custom Spell Properties In-Game using BERT supervised-trained model

TL;DR

SpellForger is a game where players craft custom spells through natural language prompts, utilizing a supervised BERT model to interpret descriptions and generate balanced, personalized spells in real time, enhancing creativity and gameplay depth.

Contribution

This work introduces a novel integration of AI-driven natural language processing for real-time spell creation within a game, demonstrating AI as a core gameplay mechanic.

Findings

Real-time spell generation based on player prompts

Effective mapping of natural language to spell parameters

Enhanced player creativity and engagement

Abstract

Introduction: The application of Artificial Intelligence in games has evolved significantly, allowing for dynamic content generation. However, its use as a core gameplay co-creation tool remains underexplored. Objective: This paper proposes SpellForger, a game where players create custom spells by writing natural language prompts, aiming to provide a unique experience of personalization and creativity. Methodology: The system uses a supervisedtrained BERT model to interpret player prompts. This model maps textual descriptions to one of many spell prefabs and balances their parameters (damage, cost, effects) to ensure competitive integrity. The game is developed in the Unity Game Engine, and the AI backend is in Python. Expected Results: We expect to deliver a functional prototype that demonstrates the generation of spells in real time, applied to an engaging gameplay loop, where player creativity is central to the experience, validating the use of AI as a direct gameplay mechanic.