oliver.obst.eu

The Third International Workshop on Guided Self-Organisation (GSO-2010) will be held at Indiana University in Bloomington, Indiana, USA, 4-6 September 2010.

The workshop is comprised of a group of researchers with diverse yet related interests, overlapping in the area of self-organizing systems and methods for characterizing those systems in ways that may ultimately allow them to be guided toward prespecified goals. Information theory and graph theory are core to many of these methods; quantifying complexity and its sources a common theme.

If interested in participating, send an extended abstract to the email addresses on the workshop web site.  Selected works from the workshop will likely be published in a special journal issue (as has been the case in the past).  More information on the GSO-2010 web site.

guided self-organisation, workshop, artificial life, ai

Our new paper describes a mathematical model for generic neural microcircuits, with potential engineering applications, as well as implications to understand how networks in biology are shaped to be optimally adapted to requirements of their environment.

Reservoir computing (RC) is a recent paradigm in the field of recurrent neural networks. Networks in RC have a sparsely and randomly connected fixed hidden layer, and only output connections are trained. RC networks have recently received increased attention as a mathematical model for generic neural microcircuits to investigate and explain computations in neocortical columns. Applied to specific tasks, their fixed random connectivity, however, leads to significant variation in performance. Few problem-specific optimization procedures are known, which would be important for engineering applications, but also in order to understand how networks in biology are shaped to be optimally adapted to requirements of their environment. We study a general network initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP). The IP-based learning uses only local learning, and its aim is to improve network performance in a self-organized way. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much more persistent memory than the other methods but are also able to perform highly nonlinear mappings. We also show that IP-based on sigmoid transfer functions is limited concerning the output distributions that can be achieved.

neurophysiology, optimisation, physiological models, recurrent neural nets, unsupervised learning, reservoir computing, echo state networks

The principle of least effort in communications has been shown, by Ferrer i Cancho and Solé, to explain emergence of power laws (e.g., Zipf’s law) in human languages. In our new paper, Origins of Scaling in Genetic Code (O. Obst, D. Polani, M. Prokopenko), published on ECAL 2009, we  apply the principle and the information-theoretic model of Ferrer i Cancho and Solé to genetic coding. The application of the principle is achieved via equating the ambiguity of signals used by “speakers” with codon usage, on the one hand, and the effort of “hearers” with needs of amino acid translation mechanics, on the other hand. The re-interpreted model captures the case of the typical (vertical) gene transfer, and confirms that Zipf’s law can be found in the transition between referentially useless systems (i.e., ambiguous genetic coding) and indexical reference systems (i.e., zero-redundancy genetic coding). As with linguistic symbols, arranging genetic codes according to Zipf’s law is observed to be the optimal solution for maximising the referential power under the effort constraints. Thus, the model identifies the origins of scaling in genetic coding — via a trade-off between codon usage and needs of amino acid translation. Furthermore, the paper extends Ferrer i Cancho ­ Solé model to multiple inputs, reaching out toward the case of horizontal gene transfer (HGT) where multiple contributors may share the same genetic coding. Importantly, the extended model also leads to a sharp transition between referentially useless systems (ambiguous HGT) and indexical reference systems (zero-redundancy HGT). Zipf’s law is also observed to be the optimal solution in the HGT case.

genetic code, evolution, language, zipf’s law, horizontal gene transfer

ICDL is a multidisciplinary conference pertaining to all subjects related to
the development and learning processes of natural and artificial systems,
including perceptual, cognitive, behavioral, emotional and all other mental
capabilities that are exhibited by humans, higher animals, and robots. Its
visionary goal is to understand autonomous development in humans and higher
animals in biological, functional, and computational terms, and to enable such
development in artificial systems. ICDL strives to bring together researchers
in neuroscience, psychology, artificial intelligence, robotics and other
related areas to encourage understanding and cross-fertilization of latest
ideas. ICDL2009 is held in Shanghai, June 5-7, 2009.
For a list of topics of see the CfP at http://www.icdl09.org/.

icdl, conference, cfp, learning, development, robotics

Special issue of the Elsevier Journal of Algorithms in Cognition, Informatics and Logic.

Submissions connected to the following non-exhaustive list of topics are particularly encouraged:

• new learning paradigms of RNNs such as unsupervised learning or reservoire learning
• biologically plausible methods
• integration of RNNs and symbolic reasoning
• universal approaches for general data structures such as sets or graphs
• methods which address the generalization ability of RNNs
• challenging applications which have the potential to be benchmark problems
• visionary papers concerning the future of RNNs

Deadline for submissions is 18th of July, 2008.

Technorati Tags: neural networks, rnn, journal, cfp, special issue, recurrent neural networks

At IPSN 2008, I’m going to present our work “Using Echo State Networks for Anomaly Detection in Underground Coal Mines”. In this work, we investigate the problem of identifying anomalies in monitoring critical gas concentrations using a sensor network in an underground coal mine. In this domain, one of the main problems is a provision of mine specific anomaly detection, with cyclical (moving) instead of flatline (static) alarm threshold levels. An additional practical difficulty in modelling a specific mine is the lack of fully labelled data of normal and abnormal situations. We present an approach addressing these difficulties based on echo state networks learning mine specific anomalies when only normal data is available. Echo state networks utilize incremental updates driven by new sensor readings, thus enabling a detection of anomalies at any time during the sensor network operation. We evaluate this approach against a benchmark — Bayes Network based anomaly detection, and observe that the quality of the overall predictions is comparable to the benchmark. However, the echo state networks maintain the same level of predictive accuracy for data from multiple sources. Therefore, the ability of echo state networks to model dynamical systems make this approach more suitable for anomaly detection and predictions in sensor networks. Check out the details here.

Technorati Tags: Sensor networks, Echo State Networks, Neural Networks, Anomaly detection, IPSN, 2008, Paper, Computer Science

The Symposium on Distributed Autonomous Robotic Systems deals with new methodologies, algorithms, hardwares, system architectures to realize advanced distributed robotic systems. Topics include but are not limited to:

Architectures for teams of robots, Ambient Intelligence, Biologically inspired systems, Control issues in multi-robot systems, Distributed decision making/problem solving, Distributed/cooperative perception, Distributed planning, Distributed task execution, Human and robot interaction, Learning and adaptation in teams of robots, Multi-robot applications in exploration, search and rescue, Mobiligence (Emergence of Intelligence through Mobility), Modular robotics, Network robotics, Performance metrics for robot teams, Reconfigurable robots, Robot societies, Self-organizing robotic systems, Sensor networks, Swarm robotics, Task allocation.

The conference takes place in Tsukuba, Ibaraki, Japan, Nov. 17-19, 2008. Full paper submission is June 30, 2008. For details, check out the web page.

Technorati Tags: CFP, conference, robotics, distributed systems, autonomous robots

There’s a special session at AHS-2008: the NASA/ESA Conference on Adaptive Hardware and Systems (June 22-25, 2008, Noordwijk, The Netherlands) on “Evolutionary and Self-Organizing Sensors, Actuators and Processing Hardware” (ESOSAPH). Recent technology has witnessed the advent of cheap ubiquitous sensing, processing and actuating capabilities for isolated, distributed or collective robotic systems. These appear in the form of intelligent materials, nano-motors and -sensors, Micro-Electro-Mechanical Systems (MEMS), grid processors, Avogadro-scale digital circuits and similar structures. Established conventional AI computation paradigms do not harness the full potential of this new type of technological ability that includes dynamic reconfiguration, addition or removal of sensors, actuators or processing hardware. Classical AI paradigms are inadequate to deal with the requirements of (more…)

Our paper “Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks” is currently being presented at the European conference on Wireless Sensor Networks (EWSN’08) in Bologna, Italy. In this paper, we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show that the Bayesian Network model can learn cyclical baselines for gas concentrations, and by this reduce false alarms usually caused by flatline thresholds. You can check out the details here.

Technorati Tags: paper, sensor networks, bayesian networks, anomaly detection, coal mines

The scope of development and learning covered by this conference includes perceptual, cognitive, behavioral, emotional and all other mental capabilities that are exhibited by humans, higher animals, artificial systems and robots. Investigations of the biological and computational mechanisms of mental development are expected to improve our understanding of the working of the whole range of mental capabilities in humans and to enable autonomous development of these highly complex capabilities by robots and other artificial systems. The International Conference on Development and Learning strives to bring together researchers in neuroscience, psychology, artificial intelligence and robotics and other related areas to encourage understanding and cross-fertilization of the latest ideas and results from the different disciplines. Full papers are due March 14. ICDL 2008 takes place Aug 9th-12 in Monterey, California. For details see http://www.icdl08.org/.