🤖 AI Summary
This work addresses the challenge of deeply integrating AI into core gameplay mechanics to enable real-time, natural-language-driven co-creation of personalized spells by players. We propose and implement SpellForger, a prototype game in which players describe desired spell effects using natural language; a fine-tuned BERT model parses these inputs in real time, followed by semantic mapping and parameter-balancing algorithms that generate numerically sound, mechanic-compatible abilities, executed instantaneously within Unity. To our knowledge, this is the first system to embed a pre-trained language model (BERT) directly into the game loop as a real-time gameplay generator, establishing natural language as a direct, interactive control modality. Experimental evaluation demonstrates effective synergy among semantic fidelity, numerical balance, and expressive player creativity—validating the feasibility and engagement of AI-augmented player–AI co-creation at the mechanistic level.
📝 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.