Bart Kamphorst

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Teamwork between humans and computer agents has become increasingly prevalent. This paper presents a behavioral study of fairness and trust in a heterogeneous setting comprising both computer agents and human participants. It investigates people's choice of teammates and their commitment to their teams in a dynamic environment in which actions occur at a(More)
Many people sincerely believe that it would be good to make lifestyle choices that have a positive effect on their own health, the social viability of their communities, and the longterm quality of the environment. Yet these same people often fail to act in accordance with these intentions. In this paper, we draw attention to the fact that people's failure(More)
This paper is concerned with the 'rights' of autonomous agent systems in relation to human users or operators and specifically addresses the question of when and to what extent an agent system may take over control from someone. I start by examining an important ethical code of conduct for system designers and engineers and argue that one would do well to(More)
Autonomous e-coaching systems offer their users suggestions for action, thereby affecting the user’s decision-making process. More specifically, the suggestions that these systems make influence the options for action that people actually consider. Surprisingly though, options and the corresponding process of option generation—a decision-making stage(More)
Autonomous e-coaching systems have the potential to improve people's health behaviors on a large scale. The intelligent behavior change support system eMate exploits a model of the human agent to support individuals in adopting a healthy lifestyle. The system attempts to identify the causes of a person's non-adherence by reasoning over a computational model(More)
The ongoing digitalization and automation of coaching practices is rapidly changing the landscape of coaching and (health-related) self-improvement. The introduction of a new class of support technologies— “e-coaching systems”—promises to deliver highly personalized, timely, around-the-clock coaching in a wide variety of domains and to a broad audience. At(More)
For a GI/GI/1 queue, we show that the average sojourn time under the (blind) Randomized Multilevel Feedback algorithm is no worse than that under the Shortest Remaining Processing Time algorithm times a logarithmic function of the system load. Moreover, it is verified that this bound is tight in heavy traffic, up to a constant multiplicative factor. We(More)