Summary of Instruction-driven Game Engines on Large Language Models, by Hongqiu Wu et al.
Instruction-Driven Game Engines on Large Language Models
by Hongqiu Wu, Yan Wang, Xingyuan Liu, Hai Zhao, Min Zhang
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach to democratizing game development is introduced through the Instruction-Driven Game Engine (IDGE) project, which enables a large language model (LLM) to follow free-form game rules and autonomously generate game-play processes. The IDGE allows users to create games by issuing simple natural language instructions, significantly lowering the barrier for game development. To tackle this challenge, we approach the learning process as a Next State Prediction task, wherein the model autoregressively predicts in-game states given player actions. Our initial progress lies in developing an IDGE for Poker, which supports various poker variants and allows for high customization of rules through natural language inputs. The engine also enables rapid prototyping of new games from minimal samples, proposing a paradigm shift in game development that relies on minimal prompt and data engineering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Instruction-Driven Game Engine (IDGE) project makes it easy for anyone to create their own games by giving simple instructions to a big language model. This means people without special training can make games, which is a great way to be creative and have fun! The IDGE works by predicting what happens next in the game based on player actions. The team has already made progress with a version of Poker, where players can customize rules and create new games quickly. This is an exciting new way for people to design and play games. |
Keywords
» Artificial intelligence » Language model » Large language model » Prompt
