Integrating human support with chatbot-based behavior change interventions raises three challenges: (1) attuning the support to an individual’s state (e.g., motivation) for enhanced engagement, (2) limiting the use of the concerning human resources for enhanced efficiency, and (3) optimizing outcomes on ethical aspects (e.g., fairness). Therefore, we conducted a study in which 679 smokers and vapers had a 20% chance of receiving human feedback between five chatbot sessions. We find that having received feedback increases retention and effort spent on preparatory activities. However, analyzing a reinforcement learning (RL) model fit on the data shows there are also states where not providing feedback is better. Even this “standard” benefit-maximizing RL model is value-laden. It not only prioritizes people who would benefit most, but also those who are already doing well and want feedback. We show how four other ethical principles can be incorporated to favor other smoker subgroups, yet, interdependencies exist.

This demo paper serves to describe a research platform for autonomous surveillance by mobile robots. System components from various technical disciplines (including robot navigation, computer vision, discrete event systems, and human-computer interaction) have been combined together into a cohesive, modular system architecture. We employ our architecture to experiment with state-of-the-art methods in planning and decision making under uncertainty, which we demonstrate enable robots to effectively patrol and respond to surveillance events in a large indoor environment.

Integrating large-scale micro-generation in distribution grids is challenging for distribution grid operators, particularly when renewable energy sources (RES) and micro-cogeneration are involved. In this paper we contend that recent developments in multiagent decision making under uncertainty can positively contribute to safe, efficient and cost-effective operation of future distribution grids.