Interactive reinforcement learning (RL) has been successfully used in various applications in
different fields, which has also motivated HCI researchers to contribute in this area. In this …

Reinforcement Learning (RL) systems achieved outstanding performance in different
domains such as Atari games, finance, healthcare, and self-driving cars. However, their …

This paper provides a starting point for the development of metacognition in a common
model of cognition. It identifies significant theoretical work on metacognition from multiple …

The ability to explain actions and decisions is often regarded as a basic ingredient of
cognitive systems. But when researchers propose methods for making AI systems …

In this article, we present work on collaborating robot teams that use verbal explanations of
their actions and intentions in order to be more understandable to the human. For this, we …

People more readily accept and trust a robot that explains its behavior in natural language.
This paper introduces Why-Plan, a method that compares the perspectives of an …

Abstract Rewards in Markov Decision Processes (MDP) define the behavior of the model.
Without a clear interpretation of what the reward function is and is not capturing, one cannot …

This paper reports on a robot controller that learns and applies a cognitively-based spatial
model as it travels in challenging, real-world indoor spaces. The model not only describes …

In this work, we focused on constructing and evaluating levels of explanation (LOE) that
address two basic aspect of HRI: 1. What information should be communicated to the user …

Reinforcement Learning (RL) is a machine learning approach based on how humans and
animals learn new behaviors by actively exploring their environment that provides them …