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The Future of Interactive Machine Learning
Kory Mathewson @korymath · Kaushik Subramanian · Mark Ho · Robert Loftin · Joseph L Austerweil · Anna Harutyunyan · Doina Precup · Layla El Asri · Matthew Gombolay · Jerry Zhu · Sonia Chernova · Charles Isbell · Patrick M Pilarski · Weng-Keen Wong · Manuela Veloso · Julie A Shah · Matthew Taylor · Brenna Argall · Michael Littman

Thu Dec 08 11:00 PM -- 09:30 AM (PST) @ Hilton Diag. Mar, Blrm. A
Event URL: http://filmnips.com/ »

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)

Author Information

Kory Mathewson @korymath (University of Alberta)
Kaushik Subramanian (Cogitai Inc.)
Mark Ho (UC Berkeley)
Robert Loftin (North Carolina State University)
Joseph L Austerweil (University of Wisconsin, Madison)

As a computational cognitive psychologist, my research program explores questions at the intersection of perception and higher-level cognition. I use recent advances in statistics and computer science to formulate ideal learner models to see how they solve these problems and then test the model predictions using traditional behavioral experimentation. Ideal learner models help us understand the knowledge people use to solve problems because such knowledge must be made explicit for the ideal learner model to successfully produce human behavior. This method yields novel machine learning methods and leads to the discovery of new psychological principles.

Anna Harutyunyan (DeepMind)
Doina Precup (McGill University / DeepMind Montreal)
Layla El Asri (Microsoft)
Matthew Gombolay (MIT)
Jerry Zhu (University of Wisconsin-Madison)
Sonia Chernova (Georgia Institute of Technology)
Charles Isbell (Georgia Tech)
Charles Isbell

Dr. Charles Isbell received his bachelor's in Information and Computer Science from Georgia Tech, and his MS and PhD at MIT's AI Lab. Upon graduation, he worked at AT&T Labs/Research until 2002, when he returned to Georgia Tech to join the faculty as an Assistant Professor. He has served many roles since returning and is now The John P. Imlay Jr. Dean of the College of Computing. Charles’s research interests are varied but the unifying theme of his work has been using machine learning to build autonomous agents who engage directly with humans. His work has been featured in the popular press, congressional testimony, and in several technical collections. In parallel, Charles has also pursued reform in computing education. He was a chief architect of Threads, Georgia Tech’s structuring principle for computing curricula. Charles was also an architect for Georgia Tech’s First-of-its’s-kind MOOC-supported MS in Computer Science. Both efforts have received international attention, and been presented in the academic and popular press. In all his roles, he has continued to focus on issues of broadening participation in computing, and is the founding Executive Director for the Constellations Center for Equity in Computing. He is an AAAI Fellow and a Fellow of the ACM. Appropriately, his citation for ACM Fellow reads “for contributions to interactive machine learning; and for contributions to increasing access and diversity in computing”.

Patrick M Pilarski (University of Alberta)
Weng-Keen Wong (Oregon State University)
Manuela Veloso (Carnegie Mellon University)
Julie A Shah (MIT)
Matthew Taylor (Washington State University)
Brenna Argall (Northwestern University)
Michael Littman (Brown University)

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