Using Personality Models as Prior Knowledge to Accelerate Learning About Stress-Coping Preferences

摘要

The management of (dis)stress is an important factor for a long and healthy life. Yet, stress affects people differently and everyone manages stress in different ways. In this paper we introduce PeSA, the Personality-enabled Stress Assistant, an agent-based application that accounts for this individualism. PeSA merges several agent techniques: Reinforcement learning is used to learn about preferences of the users, prior knowledge and knowledge transfer is applied to accelerate the learning process, agent mirroring helps to enable communication and offline functionalities. Based on these mechanisms, PeSA guides through stressful phases by proposing coping strategies that are tailored to the personality of each individual user. Users can assess these advices and thus provide a reward or punishment signal that helps PeSA to improve its suggestions.