Summary of Instruction-driven Game Engine: a Poker Case Study, by Hongqiu Wu and Xingyuan Liu and Yan Wang and Hai Zhao
Instruction-Driven Game Engine: A Poker Case Study
by Hongqiu Wu, Xingyuan Liu, Yan Wang, Hai Zhao
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes, leveraging Next State Prediction as a task. The IDGE allows users to create games simply by natural language instructions, significantly lowering the barrier for game development. By training the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios, we address the gap between stability and diversity. Our initial progress lies in developing an IDGE for Poker, which supports a wide range of poker variants and allows for highly individualized new poker games through natural language inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The IDGE project lets people create their own video games by giving instructions to a special AI model. This makes it easy for anyone to make a game without needing programming skills or experience. The team trained the AI model to predict what would happen next in a game based on player actions, which is important because small mistakes could ruin the gameplay experience. They started with a poker game and created a system that can play many different versions of poker, even allowing people to invent their own new games using just words. |
Keywords
» Artificial intelligence » Large language model
