《Pharos Night：皇冠追逐》：一款基于多智能体系统的原生AI卡组构建与战术竞技场游戏设计
"Pharos Night: Crown Pursuit": An AI-Native Deck-Building and Tactical Arena Game Design Based on Multi-Agent Systems
AI总结：
该研究设计了基于多智能体系统的原生AI卡组构建与战术竞技场游戏《Pharos Night：皇冠追逐》，利用大语言模型实现核心功能，小规模试玩显示其能提供有策略性的游戏体验，也暴露出相关挑战，展现了多智能体生成式AI在游戏设计中的潜力。
AI中文摘要：
随着生成式AI技术的进步，越来越多研究者开始探索原生AI游戏，这类游戏的玩法规则由生成式AI直接驱动。本文介绍《Pharos Night：皇冠追逐》，一款基于多智能体系统的原生AI卡组构建与战术竞技场游戏。该游戏使用大语言模型生成素材与卡牌、支持NPC决策、调解自然语言交互。游戏中，玩家收集素材，用自然语言描述期望的卡牌效果，在竞技场中选择与NPC谈判或战斗。为约束模型生成结果，系统将响应解析为结构化JSON，从预定义机制构建卡牌效果，将定性效果等级映射为设计者指定的数值。13名参与者的小规模试玩显示，该系统能提供具有策略意义且吸引人的AI驱动游戏体验，同时暴露出可预测性、透明度及玩家控制方面的挑战。本研究证明了多智能体生成式AI系统在创造更具涌现性的数字游戏体验方面的潜力。
英文摘要：
With advancements in generative AI technology, an increasing number of researchers have begun exploring AI-native games in which gameplay rules are directly driven by generative AI. This paper presents "Pharos Night: Crown Pursuit," an AI-native deck-building and tactical arena game based on a multi-agent system. The game uses large language models to generate materials and cards, support NPC decision-making, and mediate natural-language interactions. During play, players collect materials, describe desired card effects in natural language, and choose whether to negotiate or fight with NPCs in the arena. To constrain model-generated outcomes, the system parses responses as structured JSON, constructs card effects from predefined mechanics, and maps qualitative effect levels to designer-specified numerical values. A small-scale playtest with 13 participants suggests that the system can provide strategically meaningful and engaging AI-driven gameplay, while also revealing challenges related to predictability, transparency, and player control. This work demonstrates the potential of multi-agent generative AI systems for creating more emergent digital game experiences.
