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Contents

  1. Building an organic computing device with multiple interconnected brains.
  2. Table of contents
  3. Servicenavigation
  4. Frontiers | An Organic Computing Approach to Self-Organizing Robot Ensembles | Robotics and AI
  5. Table of Contents
Nachruf / Obituary: PD Dr. Rolf Würtz

Although their data is hosted on the nodes, HDCs exist independent of the individual carriers. While HDCs float between physical carriers, their corresponding HDC messages are disseminated in the network by a new effective transport protocol named. Finally, we demonstrate that HDCs detect traffic phenomena reliably and propagate them robustly within the network.

Organic Computing OC aims at handling the growing complexity in technical systems by endowing them with. OC systems with these capabilities can tolerate disturbances and continue working properly while adapting their behaviour to the changes in their environment. Layer 1 of this architecture, which is implemented using an eXtended Classifier System XCS , allows for quick response to changes if a situation appears, which is.

Thus, Layer 1 acts as a kind of memory. Layer 2 is triggered if the new situation is not covered by the population of the XCS on Layer 1. In that case, different parameter sets are evaluated using an optimisation algorithm on a simulation model of the real system. After that, the best parameter set found is given to the XCS on Layer 1 for further evaluation in the real world.

The contribution of this article is two-fold: Firstly, we present a rule combining mechanism for XCS that infers maximally general rules from the existing population to increase the on-line learning speed on Layer 1.

Secondly, we present a new population-based optimisation algorithm for Layer 2, which can be used to find high quality solutions for OC systems that operate in continuously changing environments. Furthermore, we provide experimental results for both mechanisms and show that the proposed techniques improve both the learning rate and the solution quality. In this article we present a novel two-stage method to realise a lightweight but very capable hardware implementation of a Learning Classifier System for on-chip learning.

Learning Classifier Systems LCS allow taking good run-time decisions, but current hardware implementations are either large or have limited learning capabilities. We compare our method with other LCS implementations using the multiplexer problem and evaluate it with two chip-related problems, run-time task allocation and SoC component parameterisation.

Building an organic computing device with multiple interconnected brains.

In all three problem sets, we find that the learning and self-adaptation capabilities are comparable to a full-fledged system, but with the added benefits of a lightweight hardware implementation, namely small area size and quick response time. Given our work, autonomous chips based on Learning Classifier Systems become feasible.

Humans act efficiently in a dynamic environment by learning from each other. Thus, it would be highly desirable to enable intelligent distributed systems, e. The constituents of a such a distributed system may learn in a collaborative way by communicating locally learned classification rules, for instance. This article first gives an overview of the techniques that we have developed for knowledge exchange. Then, their application is demonstrated in a realistic scenario, collaborative detection of attacks to a computer network.

Organic Computing tackles design issues of future technical systems by equipping them with self-x properties. A key self-x feature is self-optimisation, i. In this article, it is shown how self-optimisation can be realised in a safe and goal-directed way, but also why it has to be enhanced and embedded into a suitable, modular system architecture.

Table of contents

The paradigm of imitation provides a powerful means for increasing the overall learning speed in a group of robots. While separately exploring the environment in order to learn how to behave with respect to a pre-defined goal, a robot gathers experience based on its own actions and interactions with the surroundings, respectively. By accumulating additional experience via observing the behaviour of other robots, the learning process can be significantly improved in terms of speed and quality. Therefore, it benefits not only from its own actions, but also from actions that an observed robot performs.

In order to realise the imitation paradigm, we solve three main challenges, namely enabling a robot to decide whom and when to imitate, to interpret and thereby understand the behaviour of an observed robot, and to integrate the experience gathered by observation into its individual learning process. The problem of learning a generalisable model of the visual appearance of humans from video data is of major importance for computing systems interacting naturally with their users and other humans populating their environment.

We propose a step towards automatic behaviour understanding by integrating principles of Organic Computing into the posture estimation cycle, thereby relegating the need for human intervention while simultaneously raising the level of system autonomy. The system extracts coherent motion from moving upper bodies and autonomously decides about limbs and their possible spatial relationships.

Servicenavigation

The models from many videos are integrated into meta-models, which show good generalisation to different individuals, backgrounds, and attire. These models even allow robust interpretation of single video frames, where all temporal continuity is missing.


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  • [] Comparison of Self-Aware and Organic Computing Systems.

Organic Computing OC assumes that current trends and recent developments in computing, like growing interconnectedness and increasing computational power, pose new challenges to designers and users. In order to tackle the upcoming demands, OC has the vision to make systems more life-like organic by endowing them with abilities such as self-organisation, self-configuration, self-repair, or adaptation. Distributing computational intelligence by introducing concepts like self-organisation relieves the designer from exactly specifying the low-level system behaviour in all possible situations.

In addition, the user has the possibility to define a few high-level goals, rather than having to manipulate many low-level parameters. Besides the general design, we discuss several distribution variants of the architecture. The complexity of computer systems has been increasing during the past years. To control this complexity organic computing introduces the self-x features. The Organic Computing Middleware for Ubiquitous Environments eases to manage distributed computing systems by using self-configuration, self-optimisation, self-healing and self-protection.

Planning is time consuming so we introduced additionally reflexes for faster reactions. The reflexes are learned from previous plans and can be distributed to resource restricted nodes. DodOrg is a novel, biologically inspired, heterogeneous, and adaptive computer architecture, that features self-x properties in order to ease management and optimisation.

Multiple interleaved control loops, that span all system layers, are employed for realisation of these self-x properties, such as self-optimisation. A dedicated monitoring infrastructure provides the basis for these control loops and realises the basic property of self-awareness. The modular architecture of Organic Processing Cells OPC provide a flexible hardware infrastructure with reconfiguration capabilities, that are essential for realisation of adaptive systems.

This article presents an artificial hormone system for a completely decentralised realisation of self-organising task allocation. We show tight upper bounds for the real-time behaviour of self-configuration and self-healing. Mastering complexity is one of the greatest challenges for future dependable information processing systems. Traditional fault tolerance techniques relying on explicit fault models seem to be not sufficient to meet this challenge.


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During their evolution living organisms have, however, developed very effective and efficient mechanisms like the autonomic nervous system or the immune system to make them adaptive and self-organising. Thus, they are able to cope with anomalies, faults or new unforeseen situations in a safe way. Its aim is to transfer self-x properties from organic to robotic systems.

It is described in this article with a specific focus on the way ORCA deals with dynamically changing uncertainties and anomalies. Organic Computing Systems adapt to changing requirements, environment conditions or failing components. As these external influences are hard to predict, the evolution these systems undergo throughout their lifetime becomes as unpredictable.

This however stands in contrast to hard constraints, that the system may have to satisfy e. The EPOC architecture aims at bridging the gap between unpredictable evolutionary behaviour and predictability of system properties that are subject to hard system constraints. Nevertheless, design complexity and deep-submicron related reliability problems are hindering CMOS evolution.

Nano-Biological Computing – Quantum Computer Alternative!

Organic Computing is a new research direction addressing these challenges by embedding life-like principles in SoCs. It extends the functional SoC components with autonomic elements in order to build a distributed autonomic SoC. We also present how different autonomic components, including a learning classifier based decision system, adapt to changing environments and globally optimise SoC parameters. A simulation based evaluation is then presented. Urban road networks are an infrastructural key factor for modern cities. As road networks are widespread and their traffic demands are dynamically changing, adaptive and self-organising and therefore.

All presented mechanisms advance the state of the art and help to reduce the negative environmental and economical impact of traffic. We describe a distributed and self-regulated approach for the self-organisation of a large system of many self-driven, mobile objects, i. Based on methods for mobile ad-hoc networks using short-distance communication between vehicles, and ideas from distributed algorithms, we consider reactions to specific traffic structures e. Building on current models from traffic physics, we are able to develop strategies that significantly improve the flow of congested traffic.

In addition, we discuss the organic structure of urban traffic, and hint at how self-healing methods can lead to improvements in rush-hour traffic. This article presents the use of decentralised self-organisation concepts for the efficient dynamic parameterisation of hardware components and the autonomic distribution of tasks in a symmetrical multi-core processor system.

Using results obtained with an autonomic system-on-chip hardware demonstrator, we show that Learning Classifier Tables, a simplified XCS-based reinforcement learning technique optimised for a low-overhead hardware implementation and integration, achieve nearly optimal results for task-level dynamic workload balancing during run time for a standard networking application. Further investigations show the quantitative differences in optimisation quality between scenarios when local and global system information is available to the classifier rules.

Autonomic workload management or task repartitioning at run time relieves the software application developers from exploring this NP-hard problem during design time, and is able to react to dynamic and unforeseeable changes in the MPSoC operating environment. We present a protocol for distributed adaptive transmit beamforming in networks of wireless connected nodes and show that the performance of this protocol is sensitive to environmental changes. However, we show that it is possible to tune parameters of the protocol in order to compensate for these environmental aspects.

Frontiers | An Organic Computing Approach to Self-Organizing Robot Ensembles | Robotics and AI

We extend the protocol by Organic Computing principles to realise an adaptive, emergent behaviour so that optimum parameter settings for distributed environments are learned. For this organic behaviour, knowledge about the actual situation is required. To establish this situation awareness we present a novel approach to sense situations based exclusively on RF-channel measurements.

We show that an awareness of the presence, position, count and even activity of persons can be established based on simple features from the RF-channel only.

Table of Contents

Each participant will take part in the colloquium organisation and the review process for the other participants. A keynote speaker will present current research in the field.


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Details on the speaker will be available as soon as possible see website. The colloquium will give participants the opportunity to present their ongoing research in a friendly forum. One possible application is that such a nano-robot DNA walker could progress along tracks making decisions and signal when reaching the end of the track, indicating computation has finished. Just as electronic circuits are printed onto circuit boards, DNA molecules could be used to print similar tracks arranged into logical decision trees on a DNA tile, with enzymes used to control the decision branching along the tree, causing the walker to take one track or another.

DNA walkers can also carry molecular cargo, and so could be used to deliver drugs inside the body. DNA is also versatile, cheap and easy to synthesise, and computing with DNA requires much less energy than electric powered silicon processors. Its drawback is speed: it currently takes several hours to compute the square root of a four digit number, something that a traditional computer could compute in a hundredth of a second.

Another drawback is that DNA circuits are single-use, and need to be recreated to run the same computation again. Perhaps the greatest advantage of DNA over electronic circuits is that it can interact with its biochemical environment. Computing with molecules involves recognising the presence or absence of certain molecules, and so a natural application of DNA computing is to bring such programmability into the realm of environmental biosensing, or delivering medicines and therapies inside living organisms. DNA programs have already been put to medical uses, such as diagnosing tuberculosis. However, more effort is required before we can inject smart drugs directly into living organisms.