针对 WFC 难以适配动态叙事 AR 场景的问题，本文提出融合环境规则与 RL 动态图块权重调整的增强型 WFC 框架，实验证明其地图质量与沉浸感更优。
摘要：
程序化内容生成（PCG）被广泛用于在游戏中创建可扩展且多样化的环境。然而，现有方法（例如 Wave Function Collapse（WFC）算法）通常局限于静态场景，缺乏动态、叙事驱动应用所需的适应性，尤其是在增强现实（AR）游戏中。本文提出了一种面向移动 AR 环境、由强化学习增强的 WFC 框架。通过整合特定于环境的规则以及由强化学习（RL）提供依据的动态图块权重调整，所提出方法生成的地图既具有情境连贯性，又能响应游戏玩法需求。对比评估和用户研究表明，该框架实现了更优的地图质量并带来沉浸式体验，使其非常适合叙事驱动的 AR 游戏。此外，该方法还有望更广泛地应用于教育、模拟训练和沉浸式扩展现实（XR）体验，这些领域都迫切需要动态、自适应的环境。
英文摘要：
Procedural Content Generation (PCG) is widely used to create scalable and diverse environments in games. However, existing methods, such as the Wave Function Collapse (WFC) algorithm, are often limited to static scenarios and lack the adaptability required for dynamic, narrative-driven applications, particularly in augmented reality (AR) games. This paper presents a reinforcement learning-enhanced WFC framework designed for mobile AR environments. By integrating environment-specific rules and dynamic tile weight adjustments informed by reinforcement learning (RL), the proposed method generates maps that are both contextually coherent and responsive to gameplay needs. Comparative evaluations and user studies demonstrate that the framework achieves superior map quality and delivers immersive experiences, making it well-suited for narrative-driven AR games. Additionally, the method holds promise for broader applications in education, simulation training, and immersive extended reality (XR) experiences, where dynamic and adaptive environments are critical.
Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the game's challenge according to the player's capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Cont…
Personalized therapy, in which a therapeutic practice is adapted to an individual patient, leads to better health outcomes. Typically, this is accomplished by relying on a therapist's training and intuition along with feedback from a patient. While there exist approaches to automatically adapt therapeutic content to a patient, they rely on hand-authored, pre-defined rules, which may not generalize to all individuals. In this paper, we propose an approach to automatically adapt therapeutic content to patients based on physiological measures. We implement our approach in the context of arachnoph…
Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of AI at large. One of the most promising methods for achieving that long-standing goal is the use of generative AI agents, namely procedural personas, that attempt to imitate particular playing behaviors which are represented as rules, rewards, or human demonstrations. All research efforts for building those generative agents, however, have focused solely on playing behavior which is arguably a narrow perspective of wha…
Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents
Key Laboratory of Interdisciplinary Research of Computation and Economics（交叉计算与经济学交叉研究实验室）；Shanghai University of Finance and Economics（上海财经大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Chinese Academy of Sciences（中国科学院人工智能研究所）；University of Bristol（布里斯托大学）；Peking University（北京大学）；University College London（伦敦大学学院）
We introduce Adaptive Procedural Task Generation (APT-Gen), an approach to progressively generate a sequence of tasks as curricula to facilitate reinforcement learning in hard-exploration problems. At the heart of our approach, a task generator learns to create tasks from a parameterized task space via a black-box procedural generation module. To enable curriculum learning in the absence of a direct indicator of learning progress, we propose to train the task generator by balancing the agent's performance in the generated tasks and the similarity to the target tasks. Through adversarial traini…
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟（BRTA））；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；Rice University（莱斯大学）
INESC-ID（INESC-ID 研究所）；Instituto Superior Técnico, Univ. de Lisboa（里斯本大学高等技术学院）；Norwegian University of Science and Technology（挪威科技大学）；Leiden University（莱顿大学）
Automatically adapting game content to players opens new doors for game development. In this paper we propose an architecture using persona agents and experience metrics, which enables evolving procedurally generated levels tailored for particular player personas. Using our game, "Grave Rave", we demonstrate that this approach successfully adapts to four rule-based persona agents over three different experience metrics. Furthermore, the adaptation is shown to be specific in nature, meaning that the levels are persona-conscious, and not just general optimizations with regard to the selected met…
SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills
The Ohio State University（俄亥俄州立大学）；The University of Chicago（芝加哥大学）；University College London（伦敦大学学院）；University of Michigan（密歇根大学）；The Chinese University of Hong Kong（香港中文大学）；Case Western Reserve University（凯斯西储大学）；Amazon（亚马逊）
This work proposes an engine for the Creation Of Novel Adventure Narrative (CONAN), which is a procedural quest generator. It uses a planning approach to story generation. The engine is tested on its ability to create quests, which are sets of actions that must be performed in order to achieve a certain goal, usually for a reward. The engine takes in a world description represented as a set of facts, including characters, locations, and items, and generates quests according to the state of the world and the preferences of the characters. We evaluate quests through the classification of the mot…
Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
AGI Institute, School of Computer Science, Shanghai Jiao Tong University（AGI研究院，计算机科学学院，上海交通大学）；Tongyi Lab, Alibaba Group（通义实验室，阿里巴巴集团）；Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University（上海市教育委员会智能交互与认知工程重点实验室，上海交通大学）
When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Support
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）
Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game researches. Among a variety of PCG techniques, search-based approaches overwhelmingly dominate PCG development at present. While SBPCG leads to promising results and successful applications, it poses a number of challenges ranging from representation to evaluation of the content being generated. In this paper, we present an alternative yet generic PCG framework, named learning-based procedure content generation (LBPCG), to provide potential solutions to several challengin…
