Intelligent systems that learn interactively from their end-users are quickly becoming
widespread. Until recently, this progress has been fueled mostly by advances in machine …

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 …

Human-centered reinforcement learning (RL), in which an agent learns how to perform a
task from evaluative feedback delivered by a human observer, has become more and more …

Humans expect robots to learn from their feedback and adapt to their preferences. However,
there are limitations with how humans provide feedback to robots, eg, humans may give less …

Human beings are a largely untapped source of in-the-loop knowledge and guidance for
computational learning agents, including robots. To effectively design agents that leverage …

Studying teaching behavior in controlled conditions is difficult. It seems intuitive that a
human learner might have trouble reliably recreating response patterns over and over in …

When a robot is learning it needs to explore its environment and how its environment
responds on its actions. When the environment is large and there are a large number of …

Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as
weighted multidimensional scaling, support data exploration by projecting datasets to two …

Humans are extremely good at quickly teaching and learning new tasks through situated
instructions; tasks such as learning a novel game or household chore. From studying such …

Robots and other computational agents are increasingly becoming part of our daily lives.
They will need to be able to learn to perform new tasks, adapt to novel situations, and …