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

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Category IEEE TAMD 2010

Deadline: January 31, 2010 | Date: June 15, 2010

Venue/Country: SP, Germany

Updated: 2010-06-04 19:32:22 (GMT+9)

Call For Papers - CFP

Posted by: Zhengyou Zhang (zhangmicrosoft.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-activeLearningIntrinsicMotivation.htm

This special issue is jointly supported by the

IEEE CIS Technical committee on Autonomous Mental Development,

http://research.microsoft.com/en-us/um/people/zhang/amdtc/

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

manuelcabidolopesgmail.com and pierre-yves.oudeyerinria.fr

Timeline :

31 Jan 2010 - Deadline for paper submission

15 March - Notification

15 April - Final version

20 April - Electronic publication

15 June - Printed publication