AAAI Spring Symposium Series 2018

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

March 26th – 28th, 2018

Stanford University



  List of accepted papers now online !  



Overview

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

Paper Submission

We invite paper submissions on relevant topics, which include, but are not limited to:
  • Hierarchical representation, reasoning, and planning
  • Behavior synthesis and execution in robotics
  • Planning and reasoning with abstract models while ensuring executability
  • Abstraction from controls to logic
  • Execution monitoring of autonomous systems
  • Performance evaluation of executable autonomous systems
  • Integrated task and motion planning
  • Reasoning in the presence of abstraction
  • Online model learning and model improvement
  • Detecting model errors during execution
  • Integrated representation and policy learning

We invite submissions of full papers (6-8 pages) and short/position papers (2-4 pages). We also solicit system demonstrations which highlight how some of the challenges of interest to this symposium were handled.

Papers should be submitted via the easychair portal.

Technically, symposium papers are not considered archival, and a transfer of copyright to AAAI is not required. Symposium authors are free to submit their work to other venues, but they should always check with that venue to be sure they have no problem with it.


Important Dates

Paper submission October 27 November 7, 2017 (extended!)
Notification November 27, 2017
Camera-ready deadline January 23, 2018

Accepted Papers

Validation of Hierarchical Plans via Parsing of Attribute Grammars
Roman Barták, Adrien Maillard and Rafael C. Cardoso
Situated Planning for Execution Under Temporal Constraints
Michael Cashmore, Andrew Coles, Bence Cserna, Erez Karpas, Daniele Magazzeni and Wheeler Ruml
Flexible Goal-directed Agents' Behavior via DALI MASs and ASP Modules
Stefania Costantini and Giovanni De Gasperis
Creating and Using Tools in a Hybrid Cognitive Architecture
Dongkyu Choi, Son To and Pat Langley
Perspectives on the Validation and Verification of Machine Learning Systems in the Context of Highly Automated Vehicles
Werner Damm, Martin Fränzle, Sebastian Gerwinn and Paul Kröger
SiRoK: Situated Robot Knowledge - Understanding the Balance Between Situated Knowledge and Variability
Angel Daruna, Vivian Chu, Weiyu Liu, Meera Hahn, Priyanka Khante, Sonia Chernova and Andrea Thomaz
Teaching Virtual Agents to Perform Complex Spatial-Temporal Activities
Tuan Do, Nikhil Krishnaswamy and James Pustejovsky
Adversarial Regression for Stealthy Attacks in Cyber Physical Systems
Amin Ghafouri, Yevgeniy Vorobeychik and Xenofon Koutsoukos
Planning Hierarchies and their Connections to Language
Nakul Gopalan
Learning Generalized Reactive Policies using Deep Neural Networks
Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava and Pieter Abbeel
Optimal LTL Planning for Multi-Valued Logics
Mohammad Hekmatnejad and Georgios Fainekos
Constraint-Based Online Transformation of Abstract Plans into Executable Robot Actions
Till Hofmann, Victor Mataré, Stefan Schiffer, Alexander Ferrein and Gerhard Lakemeyer
Learning to Act in Partially Structured Dynamic Environment
Chen Huang and Lantao Liu
Represention, Use, and Acquisition of Affordances in Cognitive Systems
Pat Langley
Learning Planning Operators from Episodic Traces
David Menager and Dongkyu Choi
Human-Agent Teaming as a Common Problem for Goal Reasoning
Matthew Molineaux, Michael Floyd, Dustin Dannenhauer and David Aha
Interaction and Learning in a Humanoid Robot Magic Performance
Kyle Morris, John Anderson, Meng Cheng Lau and Jacky Baltes
A Framework for Complementary Video-Game Companion Character Behavior
Gavin Scott and Foaad Khosmood
Reasoning About Domains with PDDL
Alexander Shleyfman and Erez Karpas
On Chatbots Exhibiting Goal-Directed Autonomy in Dynamic Environments
Biplav Srivastava
The Real Blocks-World and Other Unsolved Problems
Siddharth Srivastava
Exploiting Micro-Clusters to Close The Loop in Data-Mining Robots for Human Monitoring
Einoshin Suzuki
Robot Behavioral Exploration and Multi-modal Perception using Dynamically Constructed Controllers
Suhua Wei, Shiqi Zhang, Jivko Sinapov, Jesse Thomason and Peter Stone
Learning Abstractions by Transferring Abstract Policies to Grounded State Spaces
Lawson L.S. Wong
Information-Efficient Model Identification for Tensegrity Robot Locomotion
Shaojun Zhu, David Surovik, Kostas Bekris and Abdeslam Boularias

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

Program Committee

Christopher Amato Northeastern University
J. Benton NASA Ames Research Center / AAMU-RISE
Joydeep Biswas University of Massachusetts Amherst
Minh Do NASA Ames Research Center
Esra Erdem Sabanci University
Georgios Fainekos Arizona State University
Alberto Finzi Universita' di Napoli Federico II
Michael Gelfond Texas Tech University
Marc Hanheide University of Lincoln
Laura Hiatt U.S. Naval Research Laboratory
Luca Iocchi Sapienza University of Rome
Leslie Kaelbling MIT
Sven Koenig University of Southern California
Lars Kunze University of Oxford
Bruno Lacerda University of Oxford
Gerhard Lakemeyer RWTH Aachen University
Daniele Magazzeni King's College London
Lenka Mudrova University of Birmingham
Tim Niemueller RWTH Aachen University
Andrea Orlandini National Research Council of Italy (ISTC-CNR)
Federico Pecora Orebro University
Subramanian Ramamoorthy The University of Edinburgh
Mark Roberts Naval Research Laboratory
Alessandro Saffiotti Orebro University
Enrico Scala ANU Research School in Computer Science
Jivko Sinapov Tufts University
Sylvie Thiebaux ANU
Yu Zhang Arizona State University
Shlomo Zilberstein University of Massachusetts Amherst