Cardiverse: Harnessing LLMs for Novel Card Game Prototyping

• URL: http://arxiv.org/abs/2502.07128v2
• Date: Tue, 09 Sep 2025 16:20:01 GMT
• Title: Cardiverse: Harnessing LLMs for Novel Card Game Prototyping
• Authors: Danrui Li, Sen Zhang, Sam S. Sohn, Kaidong Hu, Muhammad Usman, Mubbasir Kapadia,
• Abstract summary: Card games require extensive human effort in creative ideation and gameplay evaluation.<n>Recent advances in Large Language Models offer opportunities to automate these processes.<n>This paper introduces a comprehensive automated card game prototyping framework.
• Score: 9.029874576285085
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation. Recent advances in Large Language Models (LLMs) offer opportunities to automate and streamline these processes. However, it remains challenging for LLMs to design novel game mechanics beyond existing databases, generate consistent gameplay environments, and develop scalable gameplay AI for large-scale evaluations. This paper addresses these challenges by introducing a comprehensive automated card game prototyping framework. The approach highlights a graph-based indexing method for generating novel game variations, an LLM-driven system for consistent game code generation validated by gameplay records, and a gameplay AI constructing method that uses an ensemble of LLM-generated heuristic functions optimized through self-play. These contributions aim to accelerate card game prototyping, reduce human labor, and lower barriers to entry for game developers. For code repo visit this http URL https://github.com/danruili/Cardiverse

Related papers

• Automated Unity Game Template Generation from GDDs via NLP and Multi-Modal LLMs [0.0]
  This paper presents a novel framework for automated game template generation using Natural Language Processing (NLP) and multi-modal Large Language Models (LLMs)<n>We introduce an end-to-end system that parses Game Design Documents (GDDs) and extracts structured game specifications.<n>We synthesizes Unity-compatible C# code that implements the core mechanics, systems, and architecture defined in the design documentation.
  arXiv  Detail & Related papers  (2025-09-07T21:53:37Z)
• ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search [3.608541939158718]
  ScriptDoctor is a Large Language Model-driven system for automatically generating and testing games in PuzzleScript.<n>It generates and tests game design ideas in an iterative loop, where human-authored examples are used to ground the system's output.<n>It serves as a concrete example of the potential of automated, open-ended LLM-based in generating novel game content.
  arXiv  Detail & Related papers  (2025-06-06T20:40:19Z)
• Unbounded: A Generative Infinite Game of Character Life Simulation [68.37260000219479]
  We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. We leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is fully encapsulated in generative models.
  arXiv  Detail & Related papers  (2024-10-24T17:59:31Z)
• Instruction-Driven Game Engine: A Poker Case Study [53.689520884467065]
  The IDGE project aims to democratize game development by enabling a large language model to follow free-form game descriptions and generate game-play processes. We train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs.
  arXiv  Detail & Related papers  (2024-10-17T11:16:27Z)
• Open Role-Playing with Delta-Engines [50.86533710515017]
  We propose a new style of game-play to bridge self-expression and role-playing: emphopen role-playing games (ORPGs)<n>Our vision is that, in the real world, we are individually similar when we are born, but we grow into unique ones as a result of the strongly different choices we make afterward.<n>In an ORPG, we empower players with freedom to decide their own growing curves through natural language inputs, ultimately becoming unique characters.
  arXiv  Detail & Related papers  (2024-08-11T18:32:29Z)
• Grammar-based Game Description Generation using Large Language Models [12.329521804287259]
  Game Description Language (GDL) provides a standardized way to express diverse games in a machine-readable format.<n>This paper presents a novel framework that leverages Large Language Models (LLMs) to generate grammatically accurate game descriptions from natural language.
  arXiv  Detail & Related papers  (2024-07-24T16:36:02Z)
• GAVEL: Generating Games Via Evolution and Language Models [40.896938709468465]
  We explore the generation of novel games in the Ludii game description language. We train a model that intelligently mutates and recombines games and mechanics expressed as code. A sample of the generated games are available to play online through the Ludii portal.
  arXiv  Detail & Related papers  (2024-07-12T16:08:44Z)
• Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs [17.84810486479385]
  Large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players andNPCs. LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. We propose a systematic LLM-based method for automatically identifying such bugs from player game logs.
  arXiv  Detail & Related papers  (2024-06-06T20:11:08Z)
• Instruction-Driven Game Engines on Large Language Models [59.280666591243154]
  The IDGE project aims to democratize game development by enabling a large language model to follow free-form game rules. We train the IDGE in a curriculum manner that progressively increases the model's exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, a universally cherished card game.
  arXiv  Detail & Related papers  (2024-03-30T08:02:16Z)
• On Automating Video Game Regression Testing by Planning and Learning [3.746904317622708]
  We propose a method and workflow for automating regression testing of certain video game aspects. The basic idea is to use detailed game logs and incremental action model learning techniques to maintain a formal model. This paper presents the first step towards minimizing or even eliminating the need for a modeling expert in the workflow.
  arXiv  Detail & Related papers  (2024-02-16T14:28:25Z)
• GameEval: Evaluating LLMs on Conversational Games [93.40433639746331]
  We propose GameEval, a novel approach to evaluating large language models (LLMs) GameEval treats LLMs as game players and assigns them distinct roles with specific goals achieved by launching conversations of various forms. We show that GameEval can effectively differentiate the capabilities of various LLMs, providing a comprehensive assessment of their integrated abilities to solve complex problems.
  arXiv  Detail & Related papers  (2023-08-19T14:33:40Z)
• SPRING: Studying the Paper and Reasoning to Play Games [102.5587155284795]
  We propose a novel approach, SPRING, to read the game's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM) In experiments, we study the quality of in-context "reasoning" induced by different forms of prompts under the setting of the Crafter open-world environment. Our experiments suggest that LLMs, when prompted with consistent chain-of-thought, have great potential in completing sophisticated high-level trajectories.
  arXiv  Detail & Related papers  (2023-05-24T18:14:35Z)
• Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Models [68.85478477006178]
  We present a Promptable Game Model (PGM) for neural video game simulators. It allows a user to play the game by prompting it with high- and low-level action sequences. Most captivatingly, our PGM unlocks the director's mode, where the game is played by specifying goals for the agents in the form of a prompt. Our method significantly outperforms existing neural video game simulators in terms of rendering quality and unlocks applications beyond the capabilities of the current state of the art.
  arXiv  Detail & Related papers  (2023-03-23T17:43:17Z)
• Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning [1.590611306750623]
  We present tile embeddings, a unified, affordance-rich representation for tile-based 2D games. We employ autoencoders trained on the visual and semantic information of tiles from a set of existing, human-annotated games. We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.
  arXiv  Detail & Related papers  (2021-10-07T04:48:48Z)
• DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games [137.86426963572214]
  We introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL) Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames.
  arXiv  Detail & Related papers  (2020-12-03T13:53:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.