Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)

TL;DR

This paper introduces a reinforcement learning-based method for automatically personalizing arachnophobia exposure therapy by generating virtual spiders tailored to individual patients' physiological responses, improving adaptation speed and accuracy.

Contribution

It presents a novel experience-driven procedural content generation approach using reinforcement learning for personalized therapy content adaptation.

Findings

EDPCGRL adapts more quickly than search-based methods.

High accuracy in matching virtual spiders to patient responses.

Effective personalization demonstrated with virtual humans.

Abstract

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 arachnophobia exposure therapy, and rely on experience-driven procedural content generation via reinforcement learning (EDPCGRL) to generate virtual spiders to match an individual patient. In this initial implementation, and due to the ongoing pandemic, we make use of virtual or artificial humans implemented based on prior arachnophobia psychology research. Our EDPCGRL method is able to more quickly adapt to these virtual humans with high accuracy in comparison to existing, search-based EDPCG approaches.