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PROJECTS PHASE I
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
Summary:
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
Summary:
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:
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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
Summary:
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.
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