Summary of Zero-shot Reasoning: Personalized Content Generation Without the Cold Start Problem, by Davor Hafnar (1) et al.
Zero-Shot Reasoning: Personalized Content Generation Without the Cold Start Problem
by Davor Hafnar, Jure Demšar
First submitted to arxiv on: 15 Feb 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 Procedural content generation is a machine learning technique used to create new game content at lower costs. Traditional approaches require collecting large amounts of data, developing complex models, and training them, which can be time-consuming and expensive. Our research explores a more practical and generalizable approach using large language models for personalized procedural content generation. We propose levels based on gameplay data collected from individual players to match game content with player preferences. This benefits both players and developers. Our approach outperforms traditional methods in keeping players engaged. We successfully tested this method in a production setting, generating viable and engaging levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine playing games that are tailored just for you! That’s what our research is all about – making games more fun and personal. We want to make it easier for game developers to create new content without breaking the bank or spending too much time. We’re using a special type of computer program called a large language model to help us do this. By studying how players play, we can suggest levels that are just right for them. This makes the game more enjoyable and keeps players coming back. Our approach is simple and works well in real-life game development settings. |
Keywords
» Artificial intelligence » Large language model » Machine learning
