Interactive machine learning (IML) explores how intelligent agents solve a task together, often focusing on adaptable collaboration over the course of sequential decision making tasks. Past research in the field of IML has investigated how autonomous agents can learn to solve problems more effectively by making use of interactions with humans. Designing and engineering fully autonomous agents is a difficult and sometimes intractable challenge. As such, there is a compelling need for IML algorithms that enable artificial and human agents to collaborate and solve independent or shared goals. The range of real-world examples of IML spans from web applications such as search engines, recommendation systems and social media personalization, to dialog systems and embodied systems such as industrial robots and household robotic assistants, and to medical robotics (e.g. bionic limbs, assistive devices, and exoskeletons). As intelligent systems become more common in industry and in everyday life, the need for these systems to interact with and learn from the people around them will also increase.

This workshop seeks to brings together experts in the fields of IML, reinforcement learning (RL), human-computer interaction (HCI), robotics, cognitive psychology and the social sciences to share recent advances and explore the future of IML. Some questions of particular interest for this workshop include: How can recent advancements in machine learning allow interactive learning to be deployed in current real world applications? How do we address the challenging problem of seamless communication between autonomous agents and humans? How can we improve the ability to collaborate safely and successfully across a diverse set of users?

We hope that this workshop will produce several outcomes:
- A review of current algorithms and techniques for IML, and a focused perspective on what is lacking;
- A formalization of the main challenges for deploying modern interactive learning algorithms in the real world; and
- A forum for interdisciplinary researchers to discuss open problems and challenges, present new ideas on IML, and plan for future collaborations.

Topics relevant to this workshop include:
Human-robot interaction
Collaborative and/or shared control
Semi-supervised learning with human intervention
Learning from demonstration, interaction and/or observation
Reinforcement learning with human-in-the-loop
Active learning, Preference learning
Transfer learning (human-to-machine, machine-to-machine)
Natural language processing for dialog systems
Computer vision for human interaction with autonomous systems
Transparency and feedback in machine learning
Computational models of human teaching
Intelligent personal assistants and dialog systems
Adaptive user interfaces
Brain-computer interfaces (e.g. human-semi-autonomous system interfaces)
Intelligent medical robots (e.g. smart wheelchairs, prosthetics, exoskeletons)