CFProposal AISB2018

  The Society for the Study of Artificial Intelligence and Simulation for Behaviour (AISB) is soliciting proposals for symposia to be held at the AISB 2018 convention.The longest running convention on Artificial Intelligence, A...


Insurance AI Analy...

Insurance AI Analytics Summit, October 9-10, London Join us for Europe’s only AI event dedicated to insurance where 300 attendees will unite from analytics, pricing, marketing, claims and underwriting. You’ll find out how advan...


AISB 2018 Convention

  The longest running convention on Artificial Intelligence, AISB 2018 will be held at the University of Liverpool, chaired by Floriana Grasso and Louise Dennis. As in the past years, AISB 2018 will provide a unique forum for p...


AI Summit London

     The AI Summit London: The World’s Number One AI Event for Business  Date: 9-10 May 2017 Venue: Business Design Centre, London. The AI Summit is the world’s first and largest/number one conference exhibition dedicated to t...


AISB Wired Health

    AISB and WIRED events have partnered to bring together inspirational high-profile speakers. Join hundreds of healthcare, pharmaceutical and technology influencers and leaders at the 4th Annual WIRED Health event, taking pl...


Hugh Gene Loebner

  The AISB were sad to learn last week of the passing of philanthropist and inventor Hugh Gene Loebner PhD, who died peacefully in his home in New York at the age of 74.  Hugh was founder and sponsor of The Loebner Prize, an an...


AI Europe 2016

  Partnership between AISB and AI Europe 2016: Next December 5th and 6th in London, AI Europe will bring together the European AI eco-system by gathering new tools and future technologies appearing in professional fields for th...


AISB convention 2017

  In the run up to AISB2017 convention (, I've asked Joanna Bryson, from the organising team, to answer few questions about the convention and what comes with it. Mohammad Majid...


Harold Cohen

Harold Cohen, tireless computer art pioneer dies at 87   Harold Cohen at the Tate (1983) Aaron image in background   Harold Cohen died at 87 in his studio on 27th April 2016 in Encintias California, USA.The first time I hear...


Dancing with Pixies?...

At TEDx Tottenham, London Mark Bishop (the former chair of the Society) demonstrates that if the ongoing EU flagship science project - the 1.6 billion dollar "Human Brain Project” - ultimately succeeds in understanding all as...



AISB miscellaneous Bulletin Item

Submission instructions for the Autonomous Robots - Special Issue on Robot Learning

Due to multiple requests, we have decided to send out submission
instructions: In order to submit to the Autonomous Robots - Special Issue on
Robot Learning, please

1) go to
2) follow the submission instructions there.
3) Make sure that you select Special Issue on Robot Learning
for your submission.

If you happen to forget Step 3, please send us an email so that
we can correct for it.

Best wishes,
Jan Peters & Andrew Ng

PS: Please do not forget: there are less FOUR WEEKS left until the deadline for the
Autonomous Robots - Special Issue on Robot Learning and early submission will
get an expedited treatment!

Call For Papers: Autonomous Robots - Special Issue on Robot Learning
Quick Facts
Editors:                                                Jan Peters, Max Planck Institute for Biological Cybernetics,
                                                     Andrew Y. Ng, Stanford University
Journal:                                                Autonomous Robots
Submission Deadline:            November 8, 2008
Author Notification:                    March 1, 2009
Revised Manuscripts:                    June 1, 2009
Approximate Publication Date:   4th Quarter, 2009

Creating autonomous robots that can learn to act in unpredictable
environments has been a long standing goal of robotics, artificial
intelligence, and the cognitive sciences. In contrast, current
commercially available industrial and service robots mostly execute
fixed tasks and exhibit little adaptability. To bridge this gap,
machine learning offers a myriad set of methods some of which have
already been applied with great success to robotics problems. Machine
learning is also likely play an increasingly important role in
robotics as we take robots out of research labs and factory floors,
into the unstructured environments inhabited by humans and into other
natural environments.

To carry out increasingly difficult and diverse sets of tasks, future
robots will need to make proper use of perceptual stimuli such as
vision, lidar, proprioceptive sensing and tactile feedback, and
translate these into appropriate motor commands. In order to close
this complex loop from perception to action, machine learning will be
needed in various stages such as scene understanding, sensory-based
action generation, high-level plan generation, and torque level motor
control. Among the important problems hidden in these steps are
robotic perception, perceptuo-action coupling, imitation learning,
movement decomposition, probabilistic planning, motor primitive
learning, reinforcement learning, model learning, motor control, and
many others.

Driven by high-profile competitions such as RoboCup and the DARPA
Challenges, as well as the growing number of robot learning research
programs funded by governments around the world (e.g., FP7-ICT, the
euCognition initiative, DARPA Legged Locomotion and LAGR programs),
interest in robot learning has reached an unprecedented high point.
The interest in machine learning and statistics within robotics has
increased substantially; and, robot applications have also become
important for motivating new algorithms and formalisms in the machine
learning community.

In this Autonomous Robots Special Issue on Robot Learning, we intend
to outline recent successes in the application of domain-driven
machine learning methods to robotics. Examples of topics of interest
include, but are not limited to:
    learning models of robots, task or environments
    learning deep hierarchies or levels of representations from sensor
             & motor representations to task abstractions
    learning plans and control policies by imitation, apprenticeship
             and reinforcement learning
    finding low-dimensional embeddings of movement as implicit
             generative models
    integrating learning with control architectures
    methods for probabilistic inference from multi-modal sensory
             information (e.g., proprioceptive, tactile, vision)
    structured spatio-temporal representations designed for robot
    probabilistic inference in non-linear, non-Gaussian stochastic
             systems (e.g., for planning as well as for optimal or adaptive
From several recent workshops, it has become apparent that there is a
significant body of novel work on these topics. The special issue will
only focus on high quality articles based on sound theoretical
development as well as evaluations on real robot systems.

Time Line
Submission Deadline:                    November 8, 2008
Author Notification:                            March 1, 2009
Revised Manuscripts:                            June 1, 2009
Approximate Publication Date:           4th Quarter, 2009

Inquiries on this special issue should be send to one of the editors
listed below.

Jan Peters (
Senior Research Scientist, Head of the Robot Learning Laboratory
Department for Machine Learning and Empirical Inference, Max Planck
Institute for Biological Cybernetics, Tuebingen, Germany

Andrew Y. Ng (
Assistant Professor
Department of Computer Science, Stanford University, Palo Alto, USA