IEEE TAMD 2010 - Special issue on Active learning & intrinsically motivated exploration at IEEE TAMD

Special issue on Active learning & intrinsically motivated exploration at IEEE TAMD
Posted by: Zhengyou Zhang (zhang-AT-microsoft.com)
Date submitted: Nov. 22nd, 2009

Content:
IEEE Transactions on Autonomous Mental Development,
special issue on Active learning and intrinsically motivated exploration in
robots

http://www.ieee-cis.org/pubs/tamd/
http://flowers.inria.fr/tamd-activeLearningIntrins...

This special issue is jointly supported by the
IEEE CIS Technical committee on Autonomous Mental Development,
http://research.microsoft.com/en-us/um/people/zhan...
and the IEEE RAS Technical committee on Robot Learning,
http://www.learning-robots.de/

Learning techniques are increasingly being used in todays?complex robotic
system. Robots are expected to deal with a large variety of tasks, using
their high-dimensional and complex bodies, to interact with objects and
humans in an intuitive and friendly way. In this new setting, not all
relevant information is available at design time, thus self-experimentation
and learning by interacting with the physical and social world is very
important to acquire knowledge.

A major obstacle, in high and complex sensorimotor space, is that learning
can become extremely slow or even impossible without adequate exploration
strategies. To solve this problem, two main approaches are now converging.
Active learning, from statistical learning theory, where the learner
actively chooses experiments in order to collect highly informative
examples, and where expected information gain can be evaluated with either
theoretically optimal criteria or various computationally efficient
heuristics. The second approach, intrinsically motivated exploration, from
developmental psychology and recently operationalized in the developmental
robotics community, aims at building robots capable of open-ended cumulative
learning through task-independent efficient exploration of their
sensorimotor space and to refine our understanding of how children learn and
develop.

Although similar in some aspects, these two approaches differ in some of the
underlying assumptions. Active learning implicitly assumes that samples with
high uncertainty are the most informative and focuses on single tasks. On
the contrary, Intrinsic motivation has been identified by psychologists as
an innate incentive that pushes organisms to spontaneously explore
activities or situations for the sole reason that they have a certain degree
of novelty, challenge or surprise, hence the term curiosity-driven learning
sometimes used.

Several open problems exist still and the goal of this special issue is to
show state-of-the-art approaches to these problems and open new directions.
Papers should address the following, non-exhaustive, topics applied to
robotics or animal cognitive model:

How can traditional active learning heuristics be applied to robotics
problems such as motor learning, affordance learning or interaction
learning? How to select an active strategy ? Are there general purpose methods
or are they task dependent? How can active and intrinsic motivated exploration
enable long-life, task-independent learning and development? Is there a unified
formalism to both approaches? How precisely do they model human active learning
and exploration and its role in development? Can these approaches be used for
social tasks, e.g. joint-work and human-robot interaction?

Editors:
Manuel Lopes, University of Plymouth, http://www.plymouth.ac.uk/staff/mlopes
Pierre-Yves Oudeyer, INRIA, http://www.pyoudeyer.com

Two kinds of submissions are possible:
Regular papers, up to 15 double column pages ;
Correspondence papers either presenting a "perspective" that includes
insights into issues of wider scope than a regular paper but without being
highly computational in style or presenting concise description of recent
technical results, up to 8 double column pages.

Instructions for authors:
http://ieee-cis.org/pubs/tamd/authors/

We are accepting submissions through Manuscript Central at :
http://mc.manuscriptcentral.com/tamd-ieee (please select ?Active Learning
and Intrinsic Motivation as the submission type)

When submitting your manuscript, please also cc it to
manuelcabidolopes-AT-gmail.com and pierre-yves.oudeyer-AT-inria.fr

Timeline :
31 Jan 2010 - Deadline for paper submission
15 March - Notification
15 April - Final version
20 April - Electronic publication
15 June - Printed publication