*Reasoning and Learning in Real-World Systems for Long-Term Autonomy*
AAAI Fall Symposium
October 18-20, 2018
Arlington, Virginia
http://rbr.cs.umass.edu/lta
*Submission Deadline: July 31, 2018*
Over the past decade, decision-making agents have been increasingly
deployed in industrial settings, consumer products, healthcare,
education, and entertainment. The development of drone delivery
services, virtual assistants, and autonomous vehicles have highlighted
numerous challenges surrounding the operation of autonomous systems in
unstructured environments. This includes mechanisms to support
autonomous operations over extended periods of time, techniques that
facilitate the use of human assistance in learning and decision-making,
addressing the practical scalability of existing methods, relaxing
unrealistic assumptions, and alleviating safety concerns about deploying
these systems.
This symposium aims to identify the challenges and bridge the gaps
between theoretical frameworks for planning and learning in autonomous
agents and the requirements imposed by deployment in the real world. We
seek papers that find a common middle ground between theory and
applications, and analyze the lessons learned from these efforts,
particularly with respect to long-term autonomy.
*Invited Speakers*
We are grateful that Nissan Research Center Silicon Valley is sponsoring
the event, providing support for the following invited speakers and a
best paper award.
- Nick Hawes, University of Oxford
- Tom Wagner, Berkshire Grey
- Peter Wurman, Cogitai
*Paper Submissions*
We invite submissions of full papers (6-8 pages) and short papers (3-4
pages). Full papers can present novel work or summarize a collection of
recent work. Short papers can present preliminary work, describe new
real-world challenge problems, or present a position related to these
topics.
Topics of particular interest include, but are not limited to:
- Decision-making representations, models, and algorithms for the real world
- Hierarchical and multi-objective solutions for scalable planning and
learning
- Efficient integrations of task and motion planning
- Integrating planning, reasoning, and learning for long-term deployments
- Safety in real-world decision-making and learning
- Scalable multiagent and human-in-the-loop techniques
- Proactively incorporating human feedback in decision-making
- Leveraging the complimentary capabilities of humans and robots in
real-world tasks
- Evaluation metrics for long-term autonomy
- Case studies and descriptions of deployed autonomous systems
- Lessons learned from deployed applications of autonomous systems
Papers should be submitted to this symposium's track on EasyChair:
https://easychair.org/conferences/?conf=fss18.
Complete details can be found at the symposium website:
http://rbr.cs.umass.edu/lta.
*Organizing Committee*
Kyle H. Wray, University of Massachusetts Amherst
Julie A. Shah, Massachusetts Institute of Technology
Peter Stone, University of Texas at Austin
Stefan J. Witwicki, Nissan Research Center - Silicon Valley
Shlomo Zilberstein, University of Massachusetts Amherst
*Program Committee*
Joydeep Biswas, University of Massachusetts Amherst
Jeremy Frank, NASA Ames Research Center
Nick Hawes, University of Oxford
David Hsu, National University of Singapore
Erez Karpas, Technion - Israel Institute of Technology
Mykel Kochenderfer, Stanford University
Sven Koenig, University of Southern California
George Konidaris, Brown University
Abdel-Illah Mouaddib, University of Caen Normandy
Nicholas Roy, Massachusetts Institute of Technology
Reid Simmons, Carnegie Mellon University
Matthijs Spaan, Delft University of Technology
Siddharth Srivastava, Arizona State University
Kiri Wagstaff, NASA Jet Propulsion Laboratory
Shiqi Zhang, The State University of New York at Binghamton
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