<div dir="ltr">*****************************************************<br>Knowledge Representation and Reasoning in Robotics<br>AAAI Spring Symposium Series 2014<br><br>*****************************************************<br>
SCHEDULE<br><br><b><u>Paper submission: October 25, 2013</u></b><br>Notification: November 15, 2013<br>Symposium: March 24-26, 2014 at Stanford University, USA<br><br>*****************************************************<br>
<br>Robots and agents deployed in homes, offices and other complex domains are faced with the formidable challenge of representing, revising and reasoning with incomplete domain knowledge acquired from sensor inputs and human feedback. Although many algorithms have been developed for qualitatively or quantitatively representing and reasoning with knowledge, the research community is fragmented, with separate vocabularies that are increasingly making it difficult for these researchers to communicate with each other. For instance, the rich body of research in knowledge representation using logical reasoning paradigms provides appealing commonsense reasoning capabilities, but does not support probabilistic modeling of the considerable uncertainty in sensing and acting on robots. In parallel, robotics researchers are developing sophisticated probabilistic algorithms that elegantly model the uncertainty in sensing and acting on robots, but it is difficult to use such algorithms to represent and reason with commonsense knowledge. Furthermore, algorithms developed to combine logical and probabilistic reasoning do not provide the desired expressiveness for commonsense reasoning and/or do not fully support the uncertainty modeling capabilities required in robotics.<br>
<br>The objective of this symposium is to promote a deeper understanding of recent breakthroughs and challenges in the logical reasoning and probabilistic reasoning communities. We seek to encourage collaborative efforts towards building knowledge representation and reasoning architectures that support qualitative and quantitative descriptions of knowledge and uncertainty.<br>
<br>*****************************************************<br><br>TOPICS<br><br>We are interested in efforts that integrate, or motivate an integration of, logic-based and probabilistic algorithms for knowledge representation and/or commonsense reasoning on one or more robots or agents in different application domains. Topics of interest include (but are not limited to):<br>
<br> * Knowledge acquisition and representation.<br> * Combining symbolic and probabilistic representations.<br> * Reasoning about uncertainty.<br> * Reasoning with incomplete knowledge.<br> * Interactive and cooperative decision-making.<br>
* Learning and symbol grounding.<br> * Commonsense reasoning.<br><br>We also encourage the submission of papers that ground these topics in research areas such as robot vision, human-robot (and multirobot) collaboration, and robot planning.<br>
<br>*****************************************************<br><br>FORMAT & SUBMISSIONS<br><br>The symposium will consist of paper and poster presentations, invited talks, breakout sessions, and demos. Interested participants may submit: (1) regular papers (8 pages in the AAAI format); or (2) poster papers (4 pages in AAAI format). Papers may describe novel research or provide an overview of existing research. Papers may be submitted at the following web site:<br>
<a href="https://www.easychair.org/conferences/?conf=krr2014">https://www.easychair.org/conferences/?conf=krr2014</a><br><br>For more information about the symposium:<br><a href="http://www.cs.ttu.edu/~smohan/krr_aaai14/">http://www.cs.ttu.edu/~smohan/krr_aaai14/</a><br>
<br>If your research is primarily in qualitative representations for robots, please consider submitting your paper to a parallel symposium:<br><a href="http://strands-project.eu/qualitative-representations-for-robots.html">http://strands-project.eu/qualitative-representations-for-robots.html</a><br>
<br>*****************************************************<br><br>ORGANIZING COMMITTEE<br><br>Mohan Sridharan<br>Texas Tech University, USA<br><a href="mailto:mohan.sridharan@ttu.edu">mohan.sridharan@ttu.edu</a><br><br>Fangkai Yang<br>
The University of Texas at Austin, USA<br><a href="mailto:fkyang@cs.utexas.edu">fkyang@cs.utexas.edu</a><br><br>Subramanian Ramamoorthy<br>The University of Edinburgh, UK<br><a href="mailto:s.ramamoorthy@ed.ac.uk">s.ramamoorthy@ed.ac.uk</a><br>
<br>Volkan Patoglu<br>Sabanci University, Turkey<br><a href="mailto:vpatoglu@sabanciuniv.edu">vpatoglu@sabanciuniv.edu</a><br><br>Esra Erdem<br>Sabanci University, Turkey<br><a href="mailto:esraerdem@sabanciuniv.edu">esraerdem@sabanciuniv.edu</a><br>
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