[robotics-worldwide] [Meetings] CFP: Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy

classic Classic list List threaded Threaded
1 message Options
Reply | Threaded
Open this post in threaded view
|

[robotics-worldwide] [Meetings] CFP: Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy

Kyle Hollins Wray
***** *Reasoning and Learning in Real-World Systems for Long-Term
Autonomy* *****

AAAI Fall Symposium
October 18-20, 2018
Arlington, Virginia
https://urldefense.proofpoint.com/v2/url?u=http-3A__rbr.cs.umass.edu_lta&d=DwICaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9xvgQA01BTKyT1xTqgrl3yxJFGpIUCE__MX0HwM_HlM&s=y6o921LZOBYfSyu71PFia7DCWBfdyEo7yeKB5w0ATEg&e=

*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.

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://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dfss18&d=DwICaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9xvgQA01BTKyT1xTqgrl3yxJFGpIUCE__MX0HwM_HlM&s=_-GRB6uRJFSrZpW4wXnunWHaKsku18LyaJtgXvJxXso&e=.
Complete details can be found at the symposium website:
https://urldefense.proofpoint.com/v2/url?u=http-3A__rbr.cs.umass.edu_lta&d=DwICaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9xvgQA01BTKyT1xTqgrl3yxJFGpIUCE__MX0HwM_HlM&s=y6o921LZOBYfSyu71PFia7DCWBfdyEo7yeKB5w0ATEg&e=.

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

_______________________________________________
robotics-worldwide mailing list
[hidden email]
http://duerer.usc.edu/mailman/listinfo.cgi/robotics-worldwide