Perfect Fit: Identiteitsgebaseerde virtuele coach die ondersteunt bij stoppen met roken en meer bewegen - Ontwikkeling en geleerde lessen

Effectiveness of User States, Demographics and Traits in Persuading to Quit Smoking

This document is an encore abstract of the paper “Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior” presented at AAMAS 2023.

Lessons learnt in developing a virtual coach for smoking cessation and physical activity promotion

Background: Smoking and physical inactivity are two key preventable risk factors of cardiovascular disease. Yet, as with most health behaviors, they are difficult to change. In the interdisciplinary Perfect Fit project, scientists from different fields join forces to develop an evidence-based virtual coach that supports smokers in quitting...

Using a Virtual Coach to Quit Smoking: 14 Themes for User Needs

Adaptive Data-Driven Persuasive Communication for a Conversational Agent to Support Behavior Change

A human therapist continuously adapts how they persuade a client to adhere to a behavior change intervention based on theoretical expertise, past experience with the client as well as other clients, and the client's current situation. We aim at incorporating these elements into the persuasive communication of a conversational agent that acts as...

Perfect Fit: een identiteitsgebaseerde virtuele coach die ondersteunt bij stoppen met roken en meer bewegen

Duurzame gedragsverandering is moeilijk, zelfs wanneer het huidige gedrag een groot risico vormt voor de gezondheid. Gedragsverandering wordt gemakkelijker als het nieuwe gedrag past bij hoe een individu zichzelf ziet (identiteit). Virtuele coaching is veelbelovend om gedrags- en identiteitsverandering te ondersteunen, omdat het altijd...

Learning What to Attend to: Using Bisimulation Metrics to Explore and Improve Upon What a Deep Reinforcement Learning Agent Learns

Recent years have seen a surge of algorithms and architectures for deep Re-<br/>inforcement Learning (RL), many of which have shown remarkable success for<br/>various problems. Yet, little work has attempted to relate the performance of<br/>these algorithms and architectures to what the resulting deep RL agents actu-<br/>ally learn, and whether...