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Verantwortlich (Autor) für den Inhalt dieser Internetseiten ist:

Georg Martius
Am Campus 1,
3400 Klosterneuburg,
Austria

Seite: http://georg.playfulmachines.com
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Workshop: Information Theory in Artificial Life on ECAL 2015

Workshop: Information Theory in Artificial Life on ECAL 2015

Information Theory in Artificial Life

Monday 20th July 2015, 10:00 – 13:00

In the workshop “Information Theory in Artificial Life” we will discuss how Information Theory can be used to generate, motivate, understand and quantify the behaviour and other processes in artificial agents and life-like systems. Information Theory provides a language to express the required concepts and quantities in a general way, allowing to transfer them between different domains. For further details and information on how to submit and participate see our website. If you have any questions please send us an email at itialife@gmail.com.

Organised by:  Georg MartiusChristoph SalgeKeyan Ghazi-Zahedi, and Daniel Polani

Quantifying autonomous behavior

Quantifying autonomous behavior

In recent years research in autonomous robots has been more and more successful in developing algorithms for generating behavior from a generic task-independent objective. However, without a specific task it is difficult to evaluate the behavior. The same difficulty is also faced in characterizing the behavior of freely moving animals. Together with Eckehard Olbrich, I investigate methods based on information theoretic quantities that are able to deal with high-dimensional systems. An important feature of our method is to provide a length-scale-dependent quantity. This  allows to isolate the complexity of behavior on the coarse level,  on finer levels and on the noise level. We apply this method to toy examples and data from high-dimensional robotic systems.

Paper: G. Martius and E. Olbrich. Quantifying emergent behavior of autonomous robots. Entropy, 17(10):7266, 2015. [ bib | DOI | http ]
See also the Supplementary page.
Autonomous Learning

Autonomous Learning

How a machine can autonomously learn about itself, its environment and how to control them.

Workshop: Conceptual and Mathematical Foundations of Embodied Intelligence

Workshop: Conceptual and Mathematical Foundations of Embodied Intelligence

February 27 – March 01, 2013

Website

Abstract: In the last few decades, an overwhelming number of case studies produced the evidence that intelligent behavior of naturally evolved agents efficiently involves the embodiment as part of the underlying control process. Nowadays, there is no question that the exploration and exploitation of the embodiment represent important mechanisms of cognition. The shift from the classical view to the modern embodied view, also referred to as the cognitive turn, not only framed a novel way of thinking about intelligence but also identified a number of fundamental principles that intelligent systems obey. Well known examples are the principle of cheap design, morphological computation, and information self-structuring. Although there is general consensus on the intuitive meaning of such principles, the field of embodied intelligence currently lacks a formal theory. We think that the mathematical foundations of the core concepts have to be advanced and unified, in order to be able to realize and better understand cognitive systems that exploit their embodiment in an autonomous and completely intrinsic way. Information theory, dynamical systems theory, and information geometry already turned out to be useful in this regard. However, there is much more, and also much more to do.

Summarizing, the goal of the workshop is to identify the core concepts and to advance the theoretical foundations of embodied intelligence.

Invited speakers:

  • Randall D. Beer (Indiana University, USA): Information and dynamics in brain-body-environment systems
  • Paul Bourgine (École Polytechnique, France): Paradigms and models of embodied intelligence
  • Karl Friston (University College London, United Kingdom): Embodied inference and free energy
  • David Krakauer (University of Wisconsin-Madison, USA): Agents and their artefacts: a natural history of ex-bodiment
  • Thomas Metzinger (Johannes Gutenberg-Universität Mainz, Germany): Body-representation and self-consciousness: from embodiment to minimal phenomenal selfhood
  • Kevin O’Regan (Centre National de Recherche Scientifique, France): A theoretical basis for how artificial or biological agents can construct the basic notion of space
  • Frank Pasemann (Universität Osnabrück, Germany): Neurodynamics in the sensorimotor loop
  • Daniel Polani (University of Hertfordshire, United Kingdom): The role of information in formation of cognitive organization
  • Helge Ritter (Universität Bielefeld, Germany): Manual intelligence and embodiment
  • Jürgen Schmidhuber (Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, Switzerland, and Technische Universität München, Germany): Optimal AI – neural network ReNNaissance – theory of fun
  • Naftali Tishby (The Hebrew University, Israel): Information flow in sensation & action and the emergence of [reverse] hierarchies

Organizers: Nihat AyRalf DerKeyan Ghazi-ZahediGeorg Martius

 

Predictive Information Maximization

Predictive Information Maximization

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that
increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.

Paper: G. Martius, R. Der, and N. Ay. Information driven self-organization of complex robotic behaviors. PLoS ONE, 8(5):e63400, 2013.

Supplementary with videos etc

The Playful Machine

The Playful Machine

Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complex machines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? Our work provides answers by developing a general principle—homeokinesis, the dynamical symbiosis between brain, body, and environment—that is shown to drive robots to self- determined, individual development in a playful and obviously embodiment- related way: a dog-like robot starts playing with a barrier, eventually jumping or climbing over it; a snakebot develops coiling and jumping modes; humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarios when encountering an opponent.

theplayfulmachine_coverRalf and me wrote a book called “The Playful Machine – Theoretical Foundations and Practical Realization of Self-Organizing Robots” about our research.  The book also contains chapters on guided self-organization, a new method that helps to make the playful machines fit for fulfilling tasks in the real world. The book provides two levels of presentation. Students and scientific researchers interested in the field of robotics, self-organization and dynamical systems theory may be satisfied by the in-depth mathematical analysis of the principle, the bootstrapping scenarios, and the emerging behaviors. But the book additionally comes with a robotics simulator inviting also the non- scientific reader to simply enjoy the fabulous world of playful machines by performing the numerous experiments.

Visit the home page of the book or our group homepage for many videos, publications etc.