RepLearn 2013 - AAAI Workshop on Learning Rich Representations from Low-level Sensors

AAAI-13 called "LEARNING RICH REPRESENTATIONS FROM LOW-LEVEL SENSORS".
The workshop website is at:
http://www.marcpickett.com/RepLearn2013/
LEARNING RICH REPRESENTATIONS FROM LOW-LEVEL SENSORS
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.
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.
- 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.
Although we are most interested in general learning methods, we will
consider papers investigating a specific modality (e.g., vision or
sonar) with the aim of generalizing the findings to other modalities.
Also, although we are interested in submissions detecting patterns in
sensory data, we would especially like to encourage submissions
addressing how richer theories (such as entities, relations, and
causality) might be derived from sensor data.
FORMAT
This one-day workshop will begin with an explanation of the workshop's
focus and research overview. We will decompose the workshop into
themes that concern learning rich representations from sensor data:
tasks, techniques, evaluations, or demonstrations. We will include
invited talks from senior researchers who can summarize their
long-term research on this topic. We will also include one or more
panels that focus on the themes listed above, and their challenges.
SUBMISSION
You are invited to submit through EasyChair
(https://www.easychair.org/conferences/?conf=replea...).
All submissions should be in AAAI format, and must not have been
published elsewhere. Research papers should not exceed 6 pages, and
position papers should not exceed 3 pages. All submissions will be
refereed based on their relevance, originality, significance and
soundness.
ORGANIZING COMMITTEE
Marc Pickett (Naval Research Laboratory)
Ben Kuipers (University of Michigan)
Yann LeCun (New York University)
Clayton Morrison (University of Arizona)
ADDITIONAL INFORMATION
For additional information, please visit the supplemental workshop site.
http://www.marcpickett.com/RepLearn2013/