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IJCAI 2016 Workshop: Interactive Machine Learning: Connecting Humans and Machines
New York City, New York, USA
https://sites.google.com/site/ijcai2016iml/
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Important Dates
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Paper submission deadline: April 18th 2016
Notification of acceptance: May 18th 2016
Camera-ready submission deadline: June 2016
IML workshop at IJCAI 2016 in NYC: July 2016

Invited Speakers
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Maya Cakmak, University of Washington
Michael Littman, Brown University
Julie Shah, Massachusetts Institute of Technology
Peter Stone, University of Texas at Austin
Xiaojin Zhu, University of Wisconsin-Madison (Tentative)

Overview
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In recent years there has been an increased interest in the design of algorithms that facilitate machine learning with the help of human interaction. Such approaches often referred to as Interactive Machine Learning (IML), are based on a coupling of human input and machines during the learning process. Specifically IML is concerned with answering questions related to how machines can interact with people to solve problems more efficiently than autonomous systems (which often require intense engineering effort to be effective learning systems).

With the exponential growth in computing power and a focus on enhancing user-experience through technology, there exist several opportunities where humans are required to interact with machines to solve problems. Canonical applications of IML include scenarios involving humans interacting with robots to teach them to perform certain tasks, humans helping virtual agents play computer games by giving them feedback on their performance or using a teaching curriculum to guide the machine learning. However there exist a number of challenges in this area of research ranging from the choice of human interaction modality to the design of algorithms suitable for interactive learning and appropriate representations for the problem. As such these challenges span a variety of scientific disciplines and application domains like artificial intelligence, machine learning, human-computer interaction, cognitive science and robotics. The goal of the workshop is to bring together researchers in these fields to discuss the design and analysis of algorithms that facilitate Interactive Machine Learning. It is an opportunity for scientists in these disciplines to share their perspectives, discuss solutions to common problems and highlight the challenges in the field to help guide future research in IML.

The target audience for the workshop includes people who are interested in using machines to solve problems by having a human be an integral part of the learning process. This workshop serves as a platform where researchers can discuss approaches that bridge the gap between humans and machines and get the best of both worlds.

Topics
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We invite papers related to the topic of Interactive Machine Learning, i.e. learning algorithms that solve problems by interacting with and/or using information from humans. We also invite submissions that explore novel/promising applications of IML like robotics, virtual agents, online education, dialog systems, health care, security, transportation and others. Relevant submission topic areas include (but are not limited to):

- Supervised and semi-supervised learning
- Learning by demonstration and imitation learning
- Reinforcement learning with human interaction
- Interactive robot learning
- Active learning and preference learning
- Bayesian methods
- Personalized and teachable agents
- Transparency and feedback in ML
- Evaluation of interactive systems
- Intelligent interaction methodology
- Modeling people and their intentions
- Computational models of human teaching
- Multi-agent systems
- Human-in-the-loop intelligent systems

We seek broad participation from researchers in the fields of Artificial Intelligence, Machine Learning, Human-Computer Interaction, Cognitive Science, Robotics, Intelligent Interface Design, Adaptive Systems and related fields.

Submission Details
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Authors are invited to submit long papers (6 pages for main text and 1 page for references) or short papers (3 pages for main text and 1 page for references) on research relevant to the theme of the workshop. The papers should be formatted according to IJCAI formatting guidelines and submitted as a PDF document. All submissions are handled electronically through EasyChair (https://easychair.org/conferences/?conf=ijcai2016iml).

Papers will be subject to a single-blind peer review, i.e. authors can keep their names and affiliations on their submitted papers. Papers will be evaluated based on originality, technical soundness, clarity and significance. Accepted papers will be presented as talks and/or posters at the workshop.

Organizers
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Kaushik Subramanian, Georgia Institute of Technology
Heni Ben Amor, Arizona State University
Charles L. Isbell, Georgia Institute of Technology
Andrea L. Thomaz, Georgia Institute of Technology (now at University of Texas at Austin)

Contact: Kaushik Subramanian at ksubrama@cc.gatech.edu