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Heni Ben Amor <[log in to unmask]>
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Heni Ben Amor <[log in to unmask]>
Tue, 23 Feb 2016 14:16:51 -0700
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IJCAI 2016 Workshop: Interactive Machine Learning: Connecting Humans and
New York City, New York, USA

Important Dates

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

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)


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.


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

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 (

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.


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 [log in to unmask]

Heni Ben Amor, PhD
Assistant Professor of Robotics
Interactive Robotics Lab
Arizona State University

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