The bio-chemical information processing metaphor as a programming paradigm for organic computing

The project was proposed by: Dr. Peter Dittrich, Jena
Institut für Informatik Biosystemanalyse Fakultät für Informatik Universität Jena

Project website

All known life forms process information on a bio-molecular level. Examples are: signal processing in bacteria (e.g., chemotaxis), gene expression and morphogenesis, defense coordination and adaptation in the immune system, broadcasting information by the endocrine system, or finding a short route to a food source by an ant colony. This kind of information processing is known to be robust, self-organizing, adaptive, decentralized, asynchronous, fault-tolerant, and evolvable. Computation emerges out of an orchestrated interplay of many decentralized relatively simple components (molecules). This project will develop a theoretical and practical framework to exploit this bio-chemical information processing metaphor as a programming paradigm for organic computing. By doing so, we expect to make available a technology that allows to create computational systems with the properties of their biological counterpart. A couple of approaches are already using the chemical metaphor (e.g., Gamma, MGS, amorphous computing, and reaction-diffusion processors), but in accordance with Conrad’s tradeoff principle, programming a chemical computer appears to be difficult. Therefore, we will focus - beside implementing a workbench for chemical computing - on developing and evaluating different techniques of "chemical programming". Furthermore, we will develop analysis methods based on our chemical organization theory. Finally, we will evaluate the new techniques quantitatively and compare them to conventional approaches. As a demonstrator application domain we aim at sensor networks, systems biology, and virtual actors.

Embedded Performance Analysis for Organic Computing

The project was proposed by: Professor Dr.-Ing. Rolf Ernst, Braunschweig
Institut für Datentechnik und Kommunikationsnetze der Technischen Universität Braunschweig

This application for research grant deals with the formal performance analysis of organic computing systems (OC-systems) whose functions are at least in part subject to real-time constraints. As a basis, we use compositional analysis techniques that can be applied to heterogeneously structured, distributed systems in a very flexible and scalable way. Such compositional analysis is currently only available as an offline-technique for time invariant system topologies. Project goal is the extension of compositional performance analysis and its applicability to organic computing. The centralized, iterative solution of the underlying system of equations shall be replaced by a distributed, iterative solution that uses the available communication channels for coordination. The intended approach is enabled by modeling the necessary communication for distributed equation solving as a Kahn Process Network that allows for an asynchronous execution of distributed computation steps. Based an this model, an infrastructure will be constructed for the calculation of path latencies, buffer sizes and peak loads that can even be applied to strongly modifying organic Computer systems.

Digital On-Demand Computing Organism for Real-Time Systems

The project was proposed by: Professor Dr.-Ing. Jürgen Becker, Karlsruhe
Institut für Technik der Informationsverarbeitung der Universität Karlsruhe (TH)

together with:
An intrinsic feature of many biological systems is their capabilities of self-healing, self-adapting, selfconfiguring etc, or short, self-x features. In contrast, today’s computing systems hardly feature any of these characteristics even though they promise to raise computing to a new level of applicability. Our proposed approach to organic computing is tightly bound to basic self-x mechanisms as found, for example, in a human body. Starting with investigating basic biological mechanisms, we eventually derive a digital, on-demand computing organism representing the three levels, ’brain’, ‘organ’ and ‘cell’. The ’on-demand’ characteristic thereby emphasizes its responsiveness to environmental requests/changes as well as to changes resulting from the dynamics of the computing organism itself. Beginning with the brain level, a Software architecture for a robot controller with emphasis an self-x features is proposed. It closely interacts with an organic middleware at the organ level, featuring a decentralized control loop using messengers. At the cell level, a novel adaptive and dynamically reconfigurable hardware architecture is capable to implement the self-x features in an efficient way. In between, a power management system’s architecture co-ordinates brain level and cell level for ultralow power system efficiency. All levels are supplied with monitoring techniques and architectures as a prerequisite for enabling self-x features. We believe that our comprehensive approach to organic computing will represent the first step towards more adaptive, more power efficient and more flexible future embedded real-time systems. The proposed project is comprised of five research groups and a neurophysiologic expert: Prof. Becker (hardware architectures), Prof. Brinkschulte (middleware), Prof. Henkel (low power), Prof. Karl (monitoring), Prof. Wörn (robotics), and Prof. Brändle (neurophysiologic concepts).

Self-organized and self-regulation coordination of large swarm of self-navigating autonomous vehicles, as occuring in highway traffic

The project was proposed by: Professor Dr. Sandor Fekete, Braunschweig
Institut für Mathematische Optimierung Fachbereich Mathematik und Informatik Technische Universität Braunschweig

together with:
Professor Dr. Stefan Fischer, Lübeck
Institut für Telematik Technisch-Naturwissenschaftliche Fakultät Universität zu Lübeck

Project website

We propose AutoNomos, a distributed and self-regulated approach for the self-organization of a large system of many self-driven, mobile objects. Based on methods for mobile ad-hoc networks using short-distance communication between vehicles, and ideas from distributed algorithms, local data clouds are formed in reaction to specific traffic structures (e.g., traffic jams.) These Hovering Data Clouds (HDCs) are used for forming Organic Information Complexes (OICs) that are functional entities within the traffic flow, hosted by – but independent of – the individual moving vehicles (e.g., a structure detecting, indicating and monitoring the end of a traffic jam that continues to exist in place, even as the involved vehicles are replaced.) Using HDC-based OICs, we develop Adaptable Distributed Strategies (ADSs) for dealing with complex and changing traffic situations. A final goal is the extension to achieve Global Objectives for vehicles and traffic flow. Our project is based on a well-established interdisciplinary cooperation and combines practical know-how from the field of mobile ad-hoc networks with theoretical expertise from a wide range of algorithmic topics.

Organic architectures for self-organising smart pixel sensor chips

The project was proposed by: Professor Dr. Dietmar Fey, Jena
Lehrstuhl Rechnerarchitektur Fakultät für Mathematik und Informatik Universität Jena

The intended project is driven by a concrete reference to the application of working environments in future smart factories which will be characterised by a close co-operation of human and as production assistants denoted robots. The goal of the project is to develop appropriate base technologies according to organic computing principles in order to meet the high requirements which are put by such a very sensible machine-human interaction. The main requirement is to observe strict safety regulations to protect the human worker what is hardly to manage with traditional digital signal processor architectures. We expect that using organic computing principles offers better robustness and faster reply times to satisfy real-time requirements. The intended research work focuses an the developing of appropriate architectures for organic smart sensor processors. In such smart sensor systems a new organic computing paradigm is pursued which is denoted as marching pixels. Marching pixels are virtual organic units which are propagating in a pixel processor array, similar to virtual ants in ant algorithms, to carry out autonomously important pre-processing tasks, e.g. fast and robust detection of objects and of their centre points. Furthermore they shall provide necessary pre-processing steps for a software based post-processing gesture recognition. This allows the robot to assist or even to plan whole tasks in interaction with the human worker. In the project we want to create and to investigate appropriate self-organising methods for smart sensor architectures based the an the marching pixels idea. Furthermore we want to develop an appropriate technology base for the realisation of marching pixels to make smart sensors self-configuring and self-healing. This allows them to adapt independently to different tasks and to compensate the failure of components.

Model-Driven Development of Self-Organizing Control Applications

The project was proposed by: Professor Dr. Hans-Ulrich Heiß, Berlin
Fachgebiet Kommunikations- und Betriebsysteme Institut für Telekommunikationssysteme Technische Universität Berlin (Fak. IV)

together with:
  • Dr.-Ing. Gero Mühl, Berlin,
    Fachgebiet Kommunikations- und Betriebssysteme Institut für Telekommunikation Technische Universität Berlin (Fak. IV)
  • Dr.-Ing. Torben Weis, Berlin,
    Fachgebiet Kommunikations- und Betriebssysteme Institut für Telekommunikation Technische Universität Berlin (Fak. IV)
Project website

Actuator- and sensor networks (AS-NETs) will become an integral part of our living and working environment. AS-NETs consist of embedded controllers, mobile devices (e.g. PDAs, Smart- Phones), and sensors. These devices form complex wireless communication networks that are subject to unpredictable changes. Thus, it is impossible to completely predetermine the configuration at design-time, e.g. to decide which device executes which application components. Furthermore, faults and changes require constant reconfiguration. However, users do not want to administer their applications at run-time. Hence, applications have to be self-organizing in order to adapt to changing settings. For example, an application should reconfigure itself when faults occur or at least recover when faults have been removed. Self-organization requires knowledge about the application. It cannot be achieved by a middleware alone. Thus, we propose a modeldriven development approach that encapsulates the required expert knowledge into the model transformation. Non-experts can develop applications using a high-level modeling language. A model transformation inspects the application model and synthesizes application components capable of self-organization and self-stabilization.

Organic Fault-Tolerant Control Architecture for Robotic Applications

The project was proposed by: Prof. Dr.-Ing. Erik Maehle, Lübeck
Institute of Computer Engineering, Universität Lübeck

together with:
  • Dr.-Ing. Werner Brockmann Institute of Computer Engineering, Universität Lübeck
  • Dr. Karl-Erwin Großpietsch, Fraunhofer-Institut für Autonome Intelligente Systeme

Mastering complexity is one of the greatest challenges for future dependable information processing systems. Traditional fault tolerance relying on explicit fault models seems to reach its limits to meet this challenge. 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-organizing also in case of new unforeseen situations. These systems operate unconsciously and in an emergent way to make the body self-protecting, self-healing, self-optimizing and self-configuring. Inspired by these organic principles the control architecture ORCA (Organic Robot Control Architecture) shall be developed in this project. Target platforms are complex distributed embedded real-time systems, in particular autonomous mobile robots. In contrast to defining fault models explicitly, the "health status" of the system is continuously monitored by so called OCUs (Organic Control Units) which are closely attached to BCUs (Basic Control Units) implementing the regular behaviors. Based on techniques like rule-based hybrid crisp-fuzzy systems or adaptive filters, the OCUs are able to learn on-line and thus adapt to new unforeseen (fault-)situations in an emergent way. To evaluate this approach, simulations as well as experiments with real climbing robots will be carried out.

Learning to Look at Humans

The project was proposed by: Professor Dr. Christoph von der Malsburg, Bochum
Institut für Neuroinformatik, Lehrstuhl für Systembiophysik der Ruhr-Universität Bochum

Artificial vision is a typical application domain of organic computing. Learning to interpret visual input is still a great challenge. We will develop a system that can learn to find and track humans in video sequences and to recognize individuals (as long as they don’t change clothing). The system will be based on a generic data format specialized on the task only after learning; and on a general recognition mechanism (elastic graph matching). It will segment and extract models from video sequences based on a schematic definition of human figures and fuse these individual models to gradually self-organize a generic articulated model. In the process, it will construct specialized subsystems for the recognition of body parts and will integrate these subsystems with each other. To recognize individuals, the system will form person-specific models emphasizing significant differences. While achieving the specific vision goals, the project will realize many of the important objectives of organic computing in an exemplary way.

Smart Teams: Local Distributed Strategies for Self-Organizing Robotic Exploration Teams

The project was proposed by: Professor Dr. Friedhelm Meyer auf der Heide, Paderborn
Heinz Nixdorf Institut und Institut für Informatik Universität Paderborn

together with:
Professor Dr. Christian Schindelhauer, Freiburg,
Computer Networks and Telematics, Universität Freiburg

Project website

In this project we aim at laying the algorithmic foundations for a scenario where an exploration team of robots we call it a smart team has to self-organize itself in order to fulfill tasks like exploring an unknown terrain and executing work in this terrain. Examples for such a task are rescue expeditions in dangerous areas or expeditions in the oceans or on planets. The work of such a smart team has to be guided by strategies for exploration, for finding important objects, and for assigning to such an object a subgroup of robots that jointly have the capabilities necessary to process the object. The challenge is that all these tasks have to be executed by local, distributed strategies that act on the mobile network of the moving robots, and have to result in a robust, effective selforganization of the team. None of these robots ever will have more than very restricted, local knowledge about the global state of the system. Their decisions are solely based on their own observations and findings, from which a globally good behavior of the whole team has to emerge. We will analyse the quality of our strategies both theoretically, e.g. by means of competitive analysis, and experimentally by extending SAHNE, our simulation platform for communication in mobile ad hoc networks.

Organisation and Control of Self-Organising Systems in Technical Compounds

The project was proposed by: Professor Dr. Martin Middendorf, Leipzig
Fakultät für Mathematik und Informatik der Universität Leipzig

The aim of this project is to develop and investigate technically realizable self-organising systems, that are designed using principles of natural self-organising systems, with a focus an systems from social insect societies. Instead of trying to simulate or rebuild the natural systems as good as possible it is the aim of this project to explore which changes in the technical systems compared to the systems from nature are necessary or useful. Different communication media and reconfigurable systems offer the chance to clearly improve the performances of the systems with respect to their usefulness for technical applications. In this project we do fundamental research an the basic self-organising properties of such systems. Another aim of the project is it to show how compounds of self-organizable systems can be created that do not exist in nature in this form and that are technically realizable and manageable. New methods for the Organisation and control of such compounds will be developed.

Organic Traffic Control

The project was proposed by: Professor Dr.-Ing. Christian Müller-Schloer, Hannover
Institut für System- und Rechnerarchitektur Fachbereich Informatik Universität Hannover

together with:
  • Professor Dr. Hartmut Schmeck, Karlsruhe
    Institut für Angewandte Informatik und Formale Beschreibungsverfahren der Universität Karlsruhe
  • Dr. Jürgen Branke, Karlsruhe,
    Institut für Angewandte Informatik und Formale Beschreibungsverfahren der Universität Karlsruhe

Project website

It is the goal of the Organic Computing initiative to investigate emergent and self-organizing phenomena and their practical usability in technical systems. Such a technical system is characterized by a large population of relatively autonomous agents, acting and learning predominantly in their local context with some limited sensory horizon, and the co-operation of these agents to solve a global problem. Traffic control is a promising field to apply this technique. Within the project Organic Traffic Control - OTC - we will use a network of adaptive learning traffic light controllers to study the possibilities and limitations of organic decentralized control systems. The project will follow two complementary approaches to the learning problem with the goal to combine both in one system: Online learning controllers (Hannover) will adapt their behaviour based on Learning Classifier Systems receiving direct feedback from the environment. Simulation-based offline optimization (Karlsruhe) will try to find optimal control parameters by using evolutionary algorithms within a simulated environment focussing on the design of robust solutions. A combination of both ideas will use offline optimization to coarsely search the parameter space while online learning will be responsible for local fine-tuning. Both approaches will be implemented and compared using a simulated city environment. The two partners will co-operate closely in the exploration and system design work packages. Also the work on the provision of the microscopic traffic simulator and the evaluation will be shared to utilize synergies.

Quantitative Emergence - Metrics, Observation and Control Tools for Complex Organic Ensembles

The project was proposed by: Professor Dr. Hartmut Schmeck, Karlsruhe
Institut für Angewandte Informatik und Formale Beschreibungsverfahren der Universität Karlsruhe

together with:

Project website

This joint project will focus on the design of concepts and tools for the implementation of an architecture needed for the realisation of self-organising technical systems which are, at the same time reliable, robust, and adaptive. As an important prerequisite for designing such systems we have to understand the effects of emergent global behaviour in networks of intelligent autonomous units (i.e. we have to quantify emergent behaviour) and we need tools to prevent unwanted behaviour and to encourage or enforce desired positive effects. The architecture will consist of a network of autonomous units (calledproduction system), one or more observers, and one or more controllers. We shall develop an appropriate methodology for observing the (global) behaviour of the system and for quantifying and evaluating emergence effects. (This will be done mainly at Hannover). Furthermore, we have to generate adequate responses of the controller to the results of the observer in order to enable a controlled emergent behaviour within the restrictions set by some external unit (the environment). This requires an exploration of various potential ways of influencing the behaviour of a selforganising production system. (This will be done at Karlsruhe). For the initial 2 years of this project we shall abstract from realistic technical applications and will use rather simple artificial production systems exhibiting some interesting properties. Only later, during Phase II (i.e. years 3 and 4) we intend to combine the new concepts and tools for the observer and the controller with the more complex organic production system Organic Traffic Control which will be developed in the corresponding project OTC.

Multi-Objective Intrinsic Evolution of Embedded Systems

The project was proposed by: Professor Dr. Marco Platzner, Paderborn
Institut für Informatik Universität Paderborn

Project website

This project aims at the investigation and development of intrinsically evolvable embedded systems. Simulated evolution provides embedded systems with a means to react properly to unforeseen changes in the environment and the system state. In an intrinsically evolved system the evolutionary process runs together with the function under evolution on the same target platform. This is a necessary precondition for autonomous operation. While evolutionary techniques have already been applied to the design of software and hardware, intrinsic evolution is not well-investigated. We see intrinsic evolution as a promising mechanism to provide autonomous embedded systems with self-adaption and self-optimization capabilities. We will achieve our goals by combining research in natural computing algorithms with embedded system architectures. We plan to investigate and develop new models and algorithms for intrinsic evolution. Key features are the incorporation of multi-objective optimization techniques, the evolution over hardware/software boundaries, and the design of resource-aware algorithms. In architectures, we intend to develop the basic technology for self-reconfiguration of hardware and software functions. This effort will leverage cutting-edge configurable system on chip platforms. The vision behind this project is that novel algorithms paired with modern system architectures will allow us to construct future embedded systems that exhibit intelligent behavior.

Formal Modeling, Safety Analysis, and Verification of Organic Computing Applications - SAVE ORCA

The project was proposed by: Professor Dr. Wolfgang Reif, Augsburg
Institut für Informatik der Universität Augsburg

Project website

The subject of this project is a method for the systematic, top-down design and construction of highly reliable and adaptive organic computing applications. Reliability here means preservation of functional correctness, safety and security under unexpected disturbances and component failures. Adaptability in the context of this project is related to adaptive system behavior under changing requirements and modified tasks. The aim is to provide a formal framework for descriptive requirements, rigorous modeling and modular design of self-adapting and self-organizing systems. This will make design and construction of future organic systems easier and safer. As an integral part of the framework, formal analysis will improve reliability, stability, and adaptability of such systems. The approach combines formal specification and verification with failure analysis techniques and intelligent re-configuration. Example applications are ’automation engineering’ and ’mobile systems’. A formal framework for the analysis of organic computing applications will be provided and prototypical tool support will be implemented. The method will be evaluated with substantial case studies.

Architecture and Design Methodology for Autonomic System on Chip

The project was proposed by: Professor Dr. Wolfgang Rosenstiel, Tübingen
Wilhelm-Schickard-Institut Technische Informatik der Universität Tübingen

together with:

Professor Dr. Andreas Herkersdorf, München
Institut für System- und Schaltungstechnik der Technischen Universität München

Organic computing is a new research area, which has as goal building systems capable of running themselves up to a certain point. The systems of today are becoming increasingly complex and the time and the effort for designing and then maintaining these systems is too big. Organic computing proposes the introduction of systems, which can self-organize self-heal, self-optimize, self-protect. Also these systems will adapt to their environment and they will improve functionality by learning. This project aims to develop architecture and a design methodology for embedding the autonomic or organic principles in SoC (System on Chip). This new architecture, which we named ASoC (Autonomic System on Chip), will have the organic properties and this project also proposes a design methodology for obtaining these systems.

On-line Fusion of Functional Knowledge within Distributed Sensor Networks

The project was proposed by: Dr. Bernhard Sick, Passau
Fakultät für Mathematik und Informatik Lehrstuhl für Informatik Universität Passau

Humans learn from other humans and intelligent computer systems should do that likewise! Humans do not only learn facts but also rules. The latter can be seen as a more generic, abstract kind of knowledge. We refer to these kinds of knowledge as descriptive and functional knowledge, respectively. In the world of distributed, intelligent sensor networks, the exchange of descriptive knowledge between sensor nodes, i.e. knowledge describing what is seen in a nodes local environment, has been investigated since several years. We will go a step further and investigate the exchange of functional knowledge that reflects how this local environment is observed and how a node reacts on certain observations. In a steadily changing environment, where new knowledge arises or old knowledge becomes obsolete, intelligent nodes adapt on-line to their environment by means of selflearning mechanisms. The exchange of functional knowledge that is locally acquired but of global importance will lead to a certain kind of self-optimization of the overall sensor network. Moreover, the overall system will exhibit an emergent behavior. Within the scope of our project is the development of techniques for the extraction, fusion, and insertion of functional knowledge considering various types of self-learning systems. Additionally, methods for the assessment of the quality, the novelty, and the reliability of functional knowledge will be provided. As an example, the techniques will be integrated into an agent-based, distributed intrusion detection system and the emergent behavior of this system will be studied.

Energy Aware Self Organized Communication in Complex Networks

The project was proposed by: Professor Dr.-Ing. Dirk Timmermann, Rostock
Institut für Angewandte Mikroelektronik und Datentechnik der Universität Rostock

Networks surrounding us are growing and getting even more complex because of actual progresses in ubiquitous and pervasive computing and distributed wireless networking. Conventional methods and mechanisms are not suitable any more. Possible solutions can be found in nature which offers mechanisms and techniques resulting in enormous benefits with just little effort. Self organization of complex systems and the arising of complex emergent global behaviour from simple local rules are the most promising and still mysterious areas to explore. Nature works following the principle "Simple rules rule.", although this is not always obvious. The main task is to discover the simple rules and mechanisms behind the complex appearing patterns of behaviour and natural processes to make them applicable on a technological level. On the one hand, we still have less knowledge on these principles and a lot of research work is needed. We have to investigate what natural phenomena can be adapted, how they function and what advantages can be expected. On the other hand, we have to unbind ourselves from conventional engineering methods and to step on novel ways. In the context of this application, we target on exploring the interrelations between causes and effects in complex and scale free systems. Sensor networks will be used as paradigm for the biological structures and natural phenomena we want to examine. Emerging appearances like stigmergy, altruism and graceful degradation will be regarded. Our aim is the emergence of energy aware communication methods in complex networks by designing and altering local rules.

Organic Computing Middleware for Ubiquitous Environments

The project was proposed by: Professor Dr. Theo Ungerer, Augsburg
Fakultät für Angewandte Informatik der Universität Augsburg

Project website

The rising complexity of smart environments in ubiquitous computing as e.g. smart offices and smart buildings require new techniques for manageability. The properties of self-organisation, self-configuration, self-optimisation, self-healing, self-explanation, self-protection, and context-awareness demanded by the Organic Computing (OC) Initiative define ambitious goals. The proposed research project has two general targets: (1) to create an Organic Ubiquitous Middleware toolkit OC? that provides a basis for the OC goals for ubiquitous computing applications; (2) to investigate self-x techniques to fulfil the OC goals. The OC? middleware architecture will be based on a multi-level observer model with one Organic Manager per node and well-defined interfaces to the applications. The observer is the monitoring part of the Organic Manager, which is responsible to guarantee the OC properties by applying MAPE (Monitor, Analyse, Plan, Execute) cycles. An initial self-configuration based on a new cooperative selection mechanism will be followed by runtime self-optimisation to improve the initial configuration. Self-healing will be demonstrated by alive monitoring based on message observation. Computer Immunology shall be investigated for self-protection. The distributed Organic Managers act locally but communicate with each other by stress messengers piggy-backed on messages to create a globally recognisable self-organising behaviour.

Last update on: 2013-01-21 PRINTPRINTTOP