In his blogs of 2012, Dr. Göte Nyman coined Internet of Behaviors (IoB). In his idea, people's
behaviors are very good predictors of their needs, and hence technology companies can …

This article is a gentle discussion about the field of reinforcement learning in practice, about
opportunities and challenges, touching a broad range of topics, with perspectives and …

Understanding decision-making is a core goal in both neuroscience and psychology, and
computational models have often been helpful in the pursuit of this goal. While many models …

Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a
reward function justifying the behavior demonstrated by an expert agent. A well-known …

To enhance the machines' intelligence, it is important for them to learn how humans perform
tasks. In this article, the issue of online adaptive learning human behavior is addressed for a …

Abstract While Reinforcement Learning (RL) aims to train an agent from a reward function in
a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward …

Understanding a decision-maker's priorities by observing their behavior is critical for
transparency and accountability in decision processes {—} such as in healthcare. Though …

Abstract Multi-agent Credit Assignment (MCA) problem is considered as one of the critical
challenges in developing Multi-Agent Reinforcement Learning (MARL). The MCA problem …

Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …

We propose a novel formulation for the Inverse Reinforcement Learning (IRL) problem,
which jointly accounts for the compatibility with the expert behavior of the identified reward …