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

AutorSebastian Ahrndt, Marco Lützenberger, Stephen M. Prochnow, and Sahin Albayrak
QuelleJohn Thangarajah, Karl Tuyls, Catholijn Jonker, and Stacy Marsella (eds.) Proceedings of the 15th International Conferences on Autonomous Agents and Multiagent Systems (AAMAS 2016), Singapore. pp. 1485-1487. IFAAMAS. ISBN: 978-1-4503-4239-1 

Nominee for the Best System Demonstration Award presented by Ann Nowe and Shih-Fen Cheng

The management of (dis)stress is an important factor for a long and healthy life. But everyone's stress is different, so is everyone's stress coping strategy. Within this demonstration we introduce PeSA, the Personality-enabled Stress Assistant, an agent-application that accounts for this individualism bringing together several agent techniques: Reinforcement learning is applied to learn about preferences of the users, prior knowledge and knowledge transfer is used to accelerate the learning process, agent mirroring is utilized to enable communication and offline functionalities. Based on that, PeSA's main functionality is to support the user during stressful phases by advising coping strategies that fit with the personality of the user. The user can rate these advices, thus, providing a reward/punishment signal.

Poster presented @ AAMAS 2016

Click to download an A0 version of the poster.