RepLearn 2013

RepLearn 2013
Workshop on Learning Rich Representations from Low-Level Sensors

July 2013 15

AAAI 2013

Monday, July 15th, 2013 in Bellevue, Washington, USA.
In conjunction with AAAI 2013.

Motivation and Relevance

A human-level artificially intelligent agent must be able to represent and reason about the world, at some level, in terms of high-level concepts such as entities and relations. The problem of acquiring these rich high-level representations, known as the knowledge acquisition bottleneck, has long been an obstacle for achieving human-level AI. A popular approach to this problem is to handcraft these high-level representations, but this has had limited success. An alternate approach is for rich representations to be learned autonomously from low-level sensor data. Potentially, the latter approach may yield more robust representations, and should rely less on human knowledge-engineering.

Schedule


(PDFs of all accepted papers are available on AAAI's library website.)

    (08:00-08:20) Sign-in. Poster setup.

    08:30-08:45 Workshop introduction and focus
    08:45-09:30 Invited talk: Jeff Hawkins
    09:35-10:20 Invited talk: Juergen Schmidhuber [talk overview]

    10:30-11:00 Coffee Break

    11:00-11:45 Invited talk: Benjamin Kuipers
    11:45-12:05 Top-Down Abstraction Learning Using Prediction as a Supervisory Signal, Jonathan Mugan [slides]
    12:10-12:30 Two Perspectives on Learning Rich Representations from Robot Experience, Joseph Modayil [slides]

    12:30-13:30 Lunch

    13:30-13:50 Learning Perceptual Causality from Video, Amy Fire and Song-Chun Zhu [slides, poster]
    13:50-14:10 Rates for Inductive Learning of Compositional Models, Adrian Barbu, Maria Pavlovskaia, and Song-Chun Zhu [slides]
    14:15-14:30 Poster advertisements:
        Symbol Acquisition for Task-Level Planning, George Konidaris, Leslie Pack Kaelbling, and Tomas Lozano-Perez
        The Construction of Reality in a Cognitive System, Michael S. P. Miller
        Events, Interest, Segmentation, Binding and Hierarchy, Richard Rohwer
        Learning Behavior Hierarchies via High-Dimensional Sensor Projection, Simon D. Levy, Suraj Bajracharya, and Ross W. Gayler
        Building on Deep Learning, Marc Pickett
        Autonomous Hierarchical POMDP Planning from Low-Level Sensors, Shawn Squire and Marie desJardins

    14:30-15:30 Poster Session
    15:30-16:00 Coffee Break

    16:00-16:20 Representation Search through Generate and Test, Ashique Rupam Mahmood and Richard S. Sutton
    16:25-17:15 Invited talk: Generative Stochastic Networks trainable by Backprop, Yoshua Bengio [slides]
    17:20-18:00 Wrap up

    Topics

    We are interested in all parts of the bridge between low-level-sensors and rich high-level representations and their use in reasoning tasks. This includes but is not limited to:

  • Learning concept hierarchies from sensor data.
  • Representing and learning invariant concepts.
  • Postulating objects and theoretical entities.
  • Postulating relations from sensor data, when the data is not explicitly relational.
  • Learning symbolic representations from numerical sensor data.
  • High-level reasoning grounded in robotic sensors and effectors.
  • Sensor-grounded research on cognitive architectures.

    • Invited Speakers

     

    Organizing Committee

    Program Committee