用强化学习自动生成能精准引发焦虑的虚拟蜘蛛，提升恐蛛症治疗效率。
Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia Treatment
- 通过强化学习动态调整虚拟蜘蛛外观，匹配用户焦虑水平。
- 实验显示该方法比传统规则生成更有效诱发目标焦虑反应。
- 适合心理治疗师及虚拟现实疗法开发者参考应用。
个性化虚拟现实暴露疗法（VRET）治疗恐蛛症需生成能引发特定焦虑反应的虚拟蜘蛛。现有方法依赖治疗师手动挑选蜘蛛，耗时且需专业知识。虽有自动化方案，但多为规则驱动，缺乏用户适应性。为此，本文提出结合过程化内容生成（PCG）与强化学习（RL）的框架，可自动调整虚拟蜘蛛以诱发期望的焦虑反应。实验表明，该系统在诱发目标焦虑方面显著优于常见的规则基方法。
原文摘要 · Abstract (English)
The need to generate a spider to provoke a desired anxiety response arises in the context of personalized virtual reality exposure therapy (VRET), a treatment approach for arachnophobia. This treatment involves patients observing virtual spiders in order to become desensitized and decrease their phobia, which requires that the spiders elicit specific anxiety responses. However, VRET approaches tend to require therapists to hand-select the appropriate spider for each patient, which is a time-consuming process and takes significant technical knowledge and patient insight. While automated methods exist, they tend to employ rules-based approaches with minimal ability to adapt to specific users. To address these challenges, we present a framework for VRET utilizing procedural content generation (PCG) and reinforcement learning (RL), which automatically adapts a spider to elicit a desired anxiety response. We demonstrate the superior performance of this system compared to a more common rules-based VRET method.
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