Precision Medicine implies a deep understanding of inter-individual differences in health
and disease that are due to genetic and environmental factors. To acquire such …

The evolution of production systems has established major challenges in internal logistics.
In order to overcome these challenges, new automation solutions have been developed and …

Machine learning is a means to derive artificial intelligence by discovering patterns in
existing data. Here, we show that applying machine learning to ordinary human language …

Abstract Performance within Human-Autonomy Teams (HATs) is influenced by the
effectiveness of communication between humans and robots. Communication is particularly …

In this paper, we provide a comprehensive outline of the different threads of work in
Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years …

Deep reinforcement learning (DRL) has gained great success by learning directly from high-
dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of …

Intelligent robots and machines are becoming pervasive in human populated environments.
A desirable capability of these agents is to respond to goal-oriented commands by …

Reinforcement learning and symbolic planning have both been used to build intelligent
autonomous agents. Reinforcement learning relies on learning from interactions with real …

We develop a taxonomy that categorizes HRI failure types and their impact on trust to
structure the broad range of knowledge contributions. We further identify research gaps in …

Robot planning in partially observable domains is difficult, because a robot needs to
estimate the current state and plan actions at the same time. When the domain includes …