Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals

Goal-setting is commonly used in behavior change applications for physical activity. However, for goals to be effective, they need to be tailored to a user’s situation (e.g., motivation, progress). One way to obtain such goals is a collaborative process in which a healthcare professional and client set a goal together, thus making use of the...

Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior

Despite their prevalence in eHealth applications for behavior change, persuasive messages tend to have small effects on behavior. Conditions or states (e.g., confidence, knowledge, motivation) and characteristics (e.g., gender, age, personality) of persuadees are two promising components for more effective algorithms for choosing persuasive...

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.

Reinforcement Learning-Based Persuasion by a Conversational Agent for Behavior Change

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...

Using Bisimulation Metrics to Analyze and Evaluate Latent State Representations

Deep Reinforcement Learning (RL) is a promising technique towards constructing intelligent agents, but it is not always easy to understand the learning process and the factors that impact it. To shed some light on this, we analyze the Latent State Representations (LSRs) that deep RL agents learn, and compare them to what such agents should...

Reinforcement Learning-Based Persuasion by a Conversational Agent for Behavior Change

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...