evolutionary robotics

CFP: 6th Int. Conf. on the Theory and Practice of Natural Computing

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6th INTERNATIONAL CONFERENCE ON THE THEORY AND PRACTICE OF NATURAL COMPUTING (TPNC 2017)

Prague, Czech Republic

December 18-20, 2017

Organized by:

Institute of Computer Science
Czech Academy of Sciences

Faculty of Mathematics and Physics
Charles University

Research Group on Mathematical Linguistics (GRLMC)
Rovira i Virgili University

http://grammars.grlmc.com/TPNC2017/
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AIMS:

TPNC is a conference series intending to cover the wide spectrum of computational principles, models and techniques inspired by information processing in nature. TPNC 2017 will reserve significant room for young scholars at the beginning of their career and particular focus will be put on methodology. The conference aims at attracting contributions to nature-inspired models of computation, synthesizing nature by means of computation, nature-inspired materials, and information processing in nature.

VENUE:

TPNC 2017 will take place in Prague, whose historic centre is UNESCO World Heritage Site and which is home to famous attractions like the Prague Castle, the Charles Bridge, etc. The venue will be:

Faculty of Mathematics and Physics
Charles University
Ke Karlovu 3
121 16 Praha 2

SCOPE:
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CFP: ECAL 2017

ECAL 2017 – 14th EUROPEAN CONFERENCE ON ARTIFICIAL LIFE

“Artificial Life and the scientific method: Create, play, experiment, discover”

https://project.inria.fr/ecal2017/

The ECAL 2017 Organizing Committee would like to cordially invite you to submit your work to the 14th European Conference on Artificial Life (ECAL 2017), taking place on the LyonTech Campus in Lyon, France, 4-8 September 2017.

*  I M P O R T A N T   D A T E S  *

Paper submission deadline:  9th April, 2017
Notification of Acceptance:  12th May, 2017
Camera-Ready due:                 9th June, 2017
Main Conference convenes:  4-8 September, 2017

Contact email for queries:  ecal2017@inria.fr
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CFP: IEEE ALIFE 2017

IEEE ALIFE 2017

2017 IEEE Symposium on Artificial Life
http://www.ele.uri.edu/ieee-ssci2017/ALIFE.htm

as part of

2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017)
Hilton Hawaiian Village Resort, Honolulu, Hawaii
Nov. 27-Dec. 1, 2017
http://www.ieee-ssci.org

Call for papers

Artificial Life is the study of the simulation and synthesis of living systems. In particular, this science of generalized living and life-like systems provides engineering with billions of years of design expertise to learn from and exploit through the example of the evolution of organic life on earth. Increased understanding of the massively successful design diversity, complexity, and adaptability of life is rapidly making inroads into all areas of engineering and the Sciences of the Artificial. Numerous applications of ideas from nature and their generalizations from life-as-we-know-it to life-as-it-could-be continually find their way into engineering and science.

IEEE ALIFE 2017 brings together researchers working on the emerging areas of Artificial Life and Complex Adaptive Systems, aiming to understand and synthesize life-like systems and applying bio-inspired synthetic methods to other science/engineering disciplines, including Biology, Robotics, Social Sciences, among others.
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Another Master’s thesis published

The second thesis of our group has been published. Please find the title and summary below.

Minimización de la red neuronal artificial de agentes encarnados evolucionados para comunicarse referencialmente

Jorge Iván Campos Bravo

En este proyecto realizamos una minimización de la red neuronal del modelo generado por Williams et al. (2008), en dicho modelose implementan dos agentes en un ambiente mínimoen el que pueden interactuar entre ellos, pero no poseen canales especializados para comunicarse.

Su tarea es sencilla, el transmisor necesita informar al receptor la posición de un objetivo en el ambiente y el receptor necesita llegar a la posición del objetivo.

En nuestro modelo, ambos agentes utilizan la misma copia estructural de red neuronal recurrente en tiempo continuo para controlar su sistema sensorio-motor; dicha red neuronal artificial consta de tres neuronas para ambos agentes.

Se realizaron modificaciones al sistema sensorio-motor y al ambiente original para adaptar el nuevo sistema neuronal, sin perder la esencia de la motivación principal, generar comunicación referencial entre los agentes.
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Master’s thesis published

The first thesis of our group has been published. Please find the title and summary below.

Un modelo de robótica evolutiva para el reconocimiento explícito de agencialidad

Leticia Cruz Bárcenas

El estudio de la cognición social ha sido abordado principalmente desde dos perspectivas. Por un lado tenemos, el punto de vista del individualismo ampliamente usado en la cognición social, donde se plantea que la interacción y cognición social es el resultado de capacidades cognitivas individuales. Por otro lado, tenemos la perspectiva interaccionista enfocada en que el comportamiento resultante de dos o más individuos reside en los mecanismos colectivos de la interacción dinámica. A pesar de la existencia de estos enfoques, el estudio del rol en la interacción social no ha sido prioritario en las investigaciones de cognición social. Algunas de las dificultades enfrentadas en este sentido están relacionadas con la identificación de características cualitativas y cuantitativas esenciales durante el fenómeno (Lenay & Stewart, 2012).

Con el fin de tener de mejores herramientas analíticas, Auvray et al. (2009) propuso un modelo minino de cognición social que reduce este fenómeno a sus elementos más básicos. Haciendo uso de este modelo se realizó un experimento cuyo objetivo era identificar los mecanismos subyacentes debido al reconocimiento de un sujeto con intencionalidad. Los resultados mostraron que el comportamiento de los individuos propiciaba la interacción con el otro, así como la discriminación del resto de los objetos del ambiente debido a los movimientos oscilatorios individuales.

Con el fin de continuar esta línea de investigación, el presente trabajo muestra un modelo sintético que simula los resultados obtenidos en el experimento original. Utilizando robótica evolutiva se implementó un modelo para investigar la dinámica de interacción en el reconocimiento explícito de agencialidad entre agentes artificiales. El modelo demostró que existe se preserva una interacción cuando los agentes están interactuando entre ellos a pesar de que existan otros objetos/obstáculos en el ambiente.
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Course on the new cognitive science

Agentes autónomos, sistemas sociales, y la nueva ciencia cognitiva

(Alternative title: Agentes autónomos y multiagentes)

Tutor: Tom Froese

This course will introduce ongoing debates in cognitive science about our changing understanding of the mind. Instead of being thought of as a digital computer inside the brain, mind is now widely considered to be an embodied, embedded and extended activity in the world. These ideas will be illustrated based on case studies of research in agent-based models and human-computer interfaces, with special emphasis on demonstrating how social interactions and technologies shape our mind. Students are not expected to program models nor to design interfaces, but to understand the implications of the new cognitive science and to apply them to their own research interests.

The course will be taught mainly in English to better prepare students for the special terms used by leading researchers in cognitive science.

The course starts on Monday, Jan. 30, 2017. Please consult the course website for more details.

Here is a video that introduces key topics of this course:

ALIFE XV Late Breaking Abstract

Can we incorporate sleep-like interruptions into evolutionary robotics?

Mario A. Zarco-Lopez and Tom Froese

Traditional use of Hopfield networks can be divided into two main categories: (1) constraint satisfaction based on predefined a weight space, and (2) model induction based on a training set of patterns. Recently, Watson et al. (2011) have demonstrated that combining these two aspects, i.e. by inducing a model of the network’s attractors by applying Hebbian learning after constraint satisfaction, can lead to self-optimization of network connectivity. A key element of their approach is a repeated randomized reset and relaxation of network state, which has been interpreted as similar to the function of sleep (Woodward, Froese, & Ikegami, 2015). This perspective might give rise to an alternative “wake-sleep” algorithm (Hinton, Dayan, Frey, & Neal, 1995). All of this research, however, has taken place with isolated artificial neural networks, which goes against decades of work on situated robotics (Cliff, 1991). We consider the challenges involved in extending this work on sleep-like self-optimization to the dynamical approach to cognition, in which behavior is seen as emerging from the interactions of brain, body and environment (Beer, 2000).

Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 91-99.

Cliff, D. (1991). Computational neuroethology: A provisional manifesto. In J.-A. Meyer & S. W. Wilson (Eds.), From Animals to Animats (pp. 29-39). MIT Press.

Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The “wake-sleep” algorithm for unsupervised neural networks. Science, 268, 1158-1161.

Watson, R. A., Buckley, C. L., & Mills, R. (2011). Optimization in “self-modeling” complex adaptive systems. Complexity, 16(5), 17-26.

Woodward, A., Froese, T., & Ikegami, T. (2015). Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model. Neural Networks, 62, 39-46.