ChatGPT4PCG Competition: Character-like Level Generation for Science Birds
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Abstract (arXiv)
This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for ChatGPT--enabling it to generate Science Birds levels with high stability and character-like qualities--fully using their creativity as well as prompt engineering skills. ChatGPT is a conversational agent developed by OpenAI. Science Birds is selected as the competition platform because designing an Angry Birds-like level is not a trivial task due to the in-game gravity; the quality of the levels is determined by their stability. To lower the entry barrier to the competition, we limit the task to the generation of capitalized English alphabetical characters. We also allow only a single prompt to be used for generating all the characters. Here, the quality of the generated levels is determined by their stability and similarity to the given characters. A sample prompt is provided to participants for their reference. An experiment is conducted to determine the effectiveness of several modified versions of this sample prompt on level stability and similarity by testing them on several characters. To the best of our knowledge, we believe that ChatGPT4PCG is the first competition of its kind and hope to inspire enthusiasm for prompt engineering in procedural content generation.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 28 Mar 2023.
Builds on
22 of 43 references.
References · 22
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10 more
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Built on by
Citing papers per year: 2023: 2; 2024: 5; 2025: 0; 2026: 1.
8 papers.
- 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
- 2024Large Language Models and Games: A Survey and Roadmapcited by 91
- 2024Large Language Models and Video Games: A Preliminary Scoping Reviewcited by 23
- 2024ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generationcited by 5
- 2024Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMscited by 2
- 2026AI for Games in the Foundation Model Eracited by 2
- 2024Zero-Shot Reasoning: Personalized Content Generation Without the Cold Start Problemcited by 1
- 2023Physics-Based Task Generation through Causal Sequence of Physical Interactionscited by 0
- 2026AI for Games in the Foundation Model Era15 Sep 2026
- 2024ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation7 Jun 2024
- 2024Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs6 Jun 2024
- 2024Large Language Models and Video Games: A Preliminary Scoping Review5 Mar 2024
- 2024Large Language Models and Games: A Survey and Roadmap28 Feb 2024
- 2024Zero-Shot Reasoning: Personalized Content Generation Without the Cold Start Problem15 Feb 2024
- 2023Physics-Based Task Generation through Causal Sequence of Physical Interactions5 Aug 2023
- 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets29 May 2023