[robotics-worldwide] [meetings] Call for Participation: AAAI Spring Symposium on Integrating Representation, Reasoning, Learning and Execution for Goal Directed Autonomy

Siddharth Srivastava sidsrivast at gmail.com
Sat Mar 10 09:46:52 PST 2018

-- Call for Participation --

AAAI Spring Symposium on Integrating Representation, Reasoning, Learning, and Execution for Goal Directed Autonomy

March 26th – 28th, 2018

Stanford University


REGISTRATION DEADLINE: March 26th, on a first come first serve basis [register online]

Confirmed invited speakers:

David Aha, Naval Research Laboratory
Emma Brunskill, Stanford University
Jeremy Frank, NASA Ames Research Center


Recent advances in AI and robotics have led to a resurgence of interest in the objective of producing intelligent agents that help us in our daily lives. Such agents must be able to rapidly adapt to the changing goals of their users, and the changing environments in which they operate.

These requirements lead to a balancing act that most current systems have difficulty contending with: on the one hand, human interaction and computational scalability favor the use of abstracted models of problems and environments domains; on the other hand, generating goal directed behavior in the real world typically requires accurate models that are difficult to obtain and computationally hard to reason with.

This symposium addresses the core research gaps that arise in designing autonomous systems that execute their actions in complex environments using imprecise models. The sources of imprecision may range from computational pragmatism to imperfect knowledge of the actual problem domain. Some of the research directions that this symposium aims to highlight are:

* hierarchical approaches for goal directed autonomy in physically manifested intelligent systems (e.g., robotics)
* formalizations for knowledge representation and reasoning under uncertainty for real-world systems and their simulations, including those based on logic as well as on probability theory
* tradeoffs between model verisimilitude, scalability, and executability in sequential decision making
* bridging the gaps between abstract models and reality in sequential decision making
* online model learning and model improvement during execution
* identifying modeling errors during plan execution
* integrated approaches for learning representations and execution policies
* analysis and use of abstractions in autonomous reasoning and execution

Organizing Committee
Siddharth Srivastava, Arizona State University
Shiqi Zhang, Cleveland State University
Nick Hawes, University of Oxford
Erez Karpas, Technion – Israel Institute of Technology
George Konidaris, Brown University
Matteo Leonetti, University of Leeds
Mohan Sridharan, The University of Auckland
Jeremy Wyatt, University of Birmingham

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