PROJECTS PHASE II
The Bio-Chemical Information Processing Metaphor as a Programming Paradigm for Organic
Computing II
The project was proposed by: Dr. Peter Dittrich, Jena
Bio Systems Analysis Group, Department of Computer Science and Mathematics, Friedrich-Schiller-Universität
Jena
Project website
Summary:
All known life forms process information on a bio-molecular level. This project
develops 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 self-x properties of their biological counterpart. In the first project
period we have designed a workbench for chemical computing, developed a theoretically
well grounded chemical programming technique (organization oriented programming),
and evaluated this technique qualitatively on various problems like a chemical FLIP-FLOP
and the maximum independent set problem. In the second project period we will focus
on structured (i.e., non-symbolic) molecules, quantitative evaluation of the new
methods, and a demonstrator application on sensor networks.
Embedded Performance Analysis for
Organic Computing
The project was proposed by: Professor Dr.-Ing. Rolf Ernst, Braunschweig
Institute for Computer and Communication
Network Engineering, Technical University of Braunschweig
Project website
Summary:
The project develops and investigates methods for performance analysis and optimization
of organic systems that are at least in part subject to real-time requirements.
Compositional analysis and optimization techniques that were successfully applied
at design time are taken as a basis to develop online methods that continuously
observe and control the real-time constraints in a dynamically changing system.
The proposed framework combines local analysis and optimization to an emergent,
self-organizing global observation and control network that scales with the system
architecture and load situation. The architecture is automatically determined while
new sets of tasks are required to announce their resource requirements. The framework
uses a variety of techniques to avoid overload and system failure, such as performance
prediction, watch dogs, and shaping functions. The techniques are must be effective,
even if the announced requirements are incorrect. There are numerous challenges,
from the framework architecture itself to computation time bounds to algorithm iteration
control to resource overhead minimization. A complete demonstrator will be developed
on a configurable set of microprocessor boards.
AUTONOMOS: A Distributed and Self-Regulating Approach for Organizing a Large System
of Mobile Objects
The project was proposed by:
- Professor Dr.
Sandor Fekete, Braunschweig
Institut für Mathematische Optimierung Fachbereich Mathematik und Informatik
Technische Universität Braunschweig
- Professor Dr.
Stefan Fischer, Lübeck
Institut für Telematik Technisch-Naturwissenschaftliche Fakultät Universität
zu Lübeck
Project website
Summary:
The objective of our project AUTONOMOS is the development of 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., for detecting, indicating and monitoring the structure 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. While the first period deals with largely local phenomena like traffic jams, the goal for the second period is the extension of our concepts and methods to medium-sized networks, with an emphasis on the estimation of individual travel times, and implications for self-organized routing.
Generic Emergent Computing in Chip Architectures
The project was proposed by: Professor Dr. Dietmar Fey, Jena
Lehrstuhl Rechnerarchitektur, Fakultät für Mathematik und Informatik, Friedrich-Schiller-Universität
Jena
Project website
Summary:
The goal of the intended project is to expand the research work of the first working
period on applicationspecific organic computing architectures for smart CMOS cameras
towards an as far as possible generic architecture for emergent computing. Emergent
computing is e.g. based on lots of small units which generate collectively self-organising
features by local interactions. Exploiting emergence is the only possibility to
control the enormous complexity that will be given in future nanoelectronic devices
that can integrate 1 Os of millions simple processor cells in one chip. In general
a significant future is forecasted for such fine-grain computer architectures due
to their enormous inherent parallelism leading to immense processing rates. However,
emergent computing architectures are required to invoke that potential. Therefore
we want to design a generic emergent chip architecture. In order to support the
mapping of applications onto such an emergent chip architecture we want to develop
a corresponding applicable method which shall be implemented in a framework. Based
on the models of Cellular Automata and Generic Programming this framework shall
enable a semi-automatic design of emergent computing schemes on the intended chip
hardware architecture. This mapping shall be experimentally applied to the implementation
of an organic robot controller in our generic emergent hardware for the execution
of a robust and fast robot path planning scheme and hand-based gesture detection.
Furthermore we intend to introduce selfhealing mechanisms on circuit level for our
fine-grain hardware in a cooperation within the priority programme.
Model-Driven Development of Self-Organizing Control Applications
The project was proposed by:
- Professor Dr. Torben
Weis, Duisburg
Lehrstuhl für Verteilte Systeme, Fakultät Ingenieurwissenschaften, Universität Duisburg-Essen
- Professor
Dr. Hans-Ulrich Heiß, Berlin
Fachgebiet Kommunikations- und Betriebsysteme, Institut für Telekommunikationssysteme,
Technische Universität Berlin (Fak. IV)
- Dr.-Ing.
Gero Mühl, Berlin,
Fachgebiet Kommunikations- und Betriebsysteme, Institut für Telekommunikationssysteme,
Technische Universität Berlin (Fak. IV)
Project website
Summary:
Organic computing systems are expected to achieve robustness and scalability by
applying selforganization on all levels of abstraction. Our project focuses on actuator
and sensor networks (ASNets) consisting of heterogeneous devices (ranging from embedded
controllers over PDAs to fullfledged PCs) which communicate using various wired
and wireless networking technologies. In this scenario, applications must exhibit
self-x properties because neither the developer can foresee all possible faults
or configuration options nor the user is willing or even able to take over configuration
and fault handling. By applying a model-driven approach, we encapsulate the expert
knowledge necessary for implementing self-x properties into the model transformation
enabling non-experts to develop AS-Net applications. Contrarily to a pure middleware
approach, the model transformation can inspect the application model and embed additional,
application-specific knowledge into the resulting implementation facilitating self-organization,
self-stabilization, and self-optimization at runtime. In the first project phase,
we managed to achieve self-stabilization and self-organization automatically for
a restricted class of applications. In the second phase, we want to support more
complex applications (e.g., observer/controller-based applications), give guarantees
during system stabilization, integrate manual management, and do reasoning using
a formal computational model.
Architecture and Design Methodology for Autonomic Systems on Chip (ASoC)
The project was proposed by:
- Professor Dr.
Wolfgang Rosenstiel, Tübingen
Wilhelm-Schickard-Institut, Technische Informatik, Fakultät für Informations- und
Kognitionswissenschaften, Universität Tübingen
- Professor Dr. Andreas
Herkersdorf, München
Institut für System- und Schaltungstechnik, Fakultät für Elektrotechnik und Informationstechnik,
Technische Universität München
Project
website
Summary:
Organic computing is a new research area, the goal of which is to build systems
with lifelike properties composed of cooperating autonomic subsystems. As VLSI (Very
Large Scale Integrated) systems become increasingly complex, and the time and effort
to design and maintain them becomes prohibitive, a new system design paradigm becomes
necessary. Organic computing has the potential to provision VLSI systems with the
capabilities of self-organization, self-healing, and self-optimization, thereby
allowing them to adapt to their environment and improve their functionality through
online learning.
In phase 2 of our Autonomic System on Chip (ASoC) project we will design and realize
self-organizing and self-optimizing decision systems that enable current and future
SoC designs to dynamically balance their performance, power and reliability at run
time. These decision systems will be based on learning classifiers, and will be
integrated into an ASoC design methodology and architectural demonstrator. This
includes the development of utilization and performance monitors, dynamic slack
time exploitation, an organic-aware design space exploration, and an Autonomic Element
interconnect that allows for individual sub-components to communicate and thereby
perform system-wide optimizations.
Smart Teams: Local Distributed Strategies for Self-Organizing Robotic Exploration
Teams
The project was proposed by:
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 organize itself
in order to fulfill tasks like exploring an unknown terrain and executing work in
this terrain. The tasks of such a Smart Team are similar to the fundamental challenges
of all social life forms: Explore, (self-)organize, communicate, and jointly act.
The work of such a Smart Team has to be guided by strategies for exploration, and
for finding and processing important objects, the treasures, and for assigning to
such an object a subgroup of robots that jointly have the capabilities necessary
to process the object.
Our aim is to combine inspirations from social behaviour of life forms (including
humans) with state-of-the-art algorithmic techniques to tackle these problems. 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 self-organization of the team. There is no global control. Instead, the
decisions of the individual robots are solely based on their own observations and
findings, from which a robust, globally good behavior of the whole team has to emerge.
In the first phase of the project we have developed and analysed strategies for
exploration and communication. The second phase will continue this effort, and in
addition deal with scheduling type problems for processing the treasures, with minimizing
energy consumption, and with setting up an experimental platform including a 3D-animation
of the terrain which allows to observe the robots at work.
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
Project
website
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 on 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 on 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.
OTC2 - Organic Traffic Control Collaborative
The project was proposed by:
- Professor
Dr.-Ing. Christian Müller-Schloer, Hannover
Institut für System- und Rechnerarchitektur Fachbereich Informatik Universität Hannover
-
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
- Prof. Dr.
Jörg Hähner, Hannover,
Institut für Systems Engineering - System- und Rechnerarchitektur (SRA), Fakultät
für Elektrotechnik und Informatik, Leibniz Universität Hannover
Project
website
Summary:
Organic Traffic Control Collaborative (OTC2) aims at the realisation
of an organic traffic control system capable of controlling and optimising traffic
signals in urban road networks. In the predecessor project OTC an architecture for
an adaptive learning node controller has been developed. This architecture is to
be extended to allow for collaboration among the node controllers, which is a prerequisite
for a network-wide optimisation. Hierarchically organised and completely decentralised
approaches to the control problem will be considered. In the hierarchical approach,
an additional layer of observer/controller components is responsible for coordination
among the node controllers. Each additional observer/controller component is responsible
for a sub-area, collecting data on the traffic situation and influencing the node
controllers accordingly. In the completely decentralised approach, node controllers
collaborate locally by exchanging information with their neighbours to achieve a
network-wide optimisation. Both approaches will be tested and examined. Possibilities
for a fusion of both will be considered. The combination of decentralised control
pursuing fine-grained goals with higherlevel observation and control having a more
abstract point of view is expected to be applicable to a broad range of problems
worked on in the Organic Computing community.
OCCS - Observation and Control of Collaborative Systems
The project was proposed by:
-
Professor Dr. Hartmut Schmeck, Karlsruhe
Institut für Angewandte Informatik und Formale Beschreibungsverfahren der Universität
Karlsruhe
- Professor
Dr.-Ing. Christian Müller-Schloer, Hannover
Institut für System- und Rechnerarchitektur Fachbereich Informatik Universität Hannover
-
Dr. Jürgen Branke, Karlsruhe,
Institut für Angewandte Informatik und Formale Beschreibungsverfahren der Universität
Karlsruhe
- Prof. Dr.
Jörg Hähner, Hannover,
Institut für Systems Engineering - System- und Rechnerarchitektur (SRA), Fakultät
für Elektrotechnik und Informatik, Leibniz Universität Hannover
Project
website
Summary:
In Phase II the project will investigate concepts and tools for the design of distributed
observer/ controller architectures. These are necessary in order to design more
complex selforganising technical systems, which are at the same time safe, robust,
and adaptive. In Phase I we have developed a generic observer/controller architecture,
which allows for controlled self-organisation in technical scenarios. By observing
and analysing the current state of the system under observation and control (SuOC),
this architecture can be used to prevent unwanted emergent behaviours and to encourage
or enforce the desired ones. In Phase II we will focus on complex distributed scenarios,
which cannot be controlled by a single central observer/controller architecture.
Therefore, our generic architecture will be extended into a distributed multi-level
observer/controller architecture to achieve controllability of complex self-organising
and adaptive technical systems. The potential of such a multi-layer observer/controller
architecture for controlling complex distributed systems will be analysed systematically
by theoretical analysis and experimental studies. We will explore the effect of
different distributed observer/controller architectures on self-organisation, self-optimisation,
and learning capabilities of the system. A major focus will be on identifying and
analysing appropriate mechanisms for collective learning and cooperation within
the technical system. In addition to some multi-agent scenarios, a major testbed
for the validation of the project results will be the distributed application "Organic
Traffic Control" which will be developed in the corresponding project OTC2.
The generic results of OCCS are intended to be introduced into other projects of
the priority programme.
Multi-Objective Intrinsic Evolution of Embedded Systems (MOVES)
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 will provide such systems with a means to
react properly to unforeseen changes in the environment and the system resources.
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 as an adaption method
is a novel approach. We view intrinsic evolution as a promising mechanism to provide
autonomous embedded systems with self-adaptive and self-optimization capabilities.
We will achieve our goals by combining research in bio-inspired computing with modern
embedded system architectures. The main research contributions of this project are
the investigation of models and approaches for evolutionary self-adaption and addressing
the challenging problems of scalability and validation of evolvable hardware. In
architectures, we will develop an adaptive system-on-chip platform leveraging cutting-edge
reconfigurable hardware technology. We will demonstrate our approaches by two case
studies, an evolvable prosthetic hand controller and autonomous robot navigation.
The vision behind this project is that novel bio-inspired algorithms paired with
modern system-on-chip architectures will allow us to construct future embedded systems
that exhibit intelligent behavior.
A Modular Approach for Evolving Societies of Learning Autonomous Systems
The project was proposed by:
Summary:
In this project we plan to develop a modular approach for realizing self-organizing
and selfoptimizing autonomous systems that show emergent behavior in societies of
such systems. Current approaches already deal with the question how individual systems
cope with failures and provide first solutions for individual self-adaptation. However
it is unclear how a system's individual self-adaptation influences the behavior
and performance of the entire system society. In this proposal we will investigate
how a system can learn to use its capabilities in changing environments while at
the same time paying attention to the overall group behavior. We present a modular
approach where a system is able to learn a model of itself and its environment including
its group members that can be used to predict which adaptation alternatives are
most promising in a certain situation. For behavior assessment we propose decentralized
evaluation functions based on socio-biological paradigms like emotions and drives
that do not only consider the system's own behavior and improvement of its own state
but also take into account its group's behavior and goals. To support fast adaptation
of a system's behavior we plan to combine individual exploration with imitation
of successful behaviors of team mates. Furthermore we plan to investigate how group
behavior emerges from such imitation and how such emergent behavior can be characterized
for instance in terms of group clustering or performance. The developed modular
approach that supports these features shall be evaluated in simulation and by our
Paderkicker robots.
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:
Original goals - The goal o this project is to develop a method for the systematic,
top-down design, construction and analysis 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 building
self-x systems, which will make design and construction of future organic systems
easier and safer. As an integral part of the framework, formal analysis will (provably)
improve the quality, reliability, stability, and adaptability of such systems. The
approach combines safety analysis, formal specification and verification with state-of-the-art
software engineering methods.
State of the project after the first phase - The project is well on track. During
the first we got some very promising results. One highlight is a general formal
specification and verification paradigm for systems with self-x properties, called
the "Restore-Invariant-Approach" Invariants encode the desired behavior which the
application system is expected to keep up under all circumstances. Temporary violation
of the invariants (e.g. because of component failures) triggers the self-organization
process in order to finally restore the invariants again. The Restore-Invariant-Approach
solves on the major challenges in applying OC systems in technical applications:
it reconciles the benefits of unforeseen decisions of self-x systems with behavioral
guarantees, technical applications can rely on. The Restore-Invariant-Approach induces
a canonical system architecture separating the productive system from the self-x
infrastructure, and the Observer/Controller part of an OC application. Existing
temporal formalisms were extended such that self-x properties may be modeled and
verified conveniently. Another strong point of the first phase is the development
of a technique to access the self-x capacity embodies in an OC application. It is
based on deductive cause-consequence analysis (DCCA), and can be used for safety-
and robustness analysis of OC systems. With adaptive DCCA it is possible to show
that self-x mechanisms can indeed add new potential to classical redundancy- and
fault-tolerance techniques. All results were applied to a reference case study from
production automation. Several other projects have shown interest in applying SAVE
ORCA analysis methods onto their own OC systems. Finally, we have been able to characterize
the class of self-x systems for which our results apply. It is described using a
UML domain meta model. It turned out that this characterization paves the way for
a smooth integration of our formal approach into an accompanying software development
process for OC applications.
The goals for the next phase are many fold. We still feel the need to consolidate
and to enhance the formal underpinnings of the Restore-Invariant-Approach and to
solve questions left open so far. While in the first phase we mainly focused on
systems adapting to failures and disturbances, one of the central challenges in
the next phase is to tackle the problem of self-adaptation of systems to changing
requirements and changing system structure. This requires the extension and improvement
of our results of the first phase, particularly with respect to the analysis techniques.
Furthermore, the idea of smoothly integrating the formal approach with a tailor-made
software engineering process via meta modeling the application domain will be pursued.
In addition we continue the evaluation of all the results on the organic production
cell case study. Another important part of the next project phase is the interaction
and cooperation with other projects in the DFG program as well as the cooperation
with our industrial partners. This will show the generality and applicability of
SAVE ORCA methods to different OC domains and improve the quality of the analyzed
system through the use of formal methods.
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 node's 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. Additionally, methods for the assessment of the quality, the novelty,
and the reliability of functional knowledge will be provided. Techniques for functional
knowledge exchange are applicable not only in the field of sensor networks, but
in the areas of software agents (e.g., in an agent-based, distributed intrusion
detection system) or robotics (e.g., in robot teams such as in the RoboCup application)
as well.
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
Project
website
Summary:
Large networks of wireless sensor nodes are a well-known vision of current research.
The idea is to incorporate self-x properties in those networks. Thereby, the nodes
shall be enabled to find the best routing path through the network autonomously
(self-organizing), they also shall be able to detect failures in adjacent nodes
and react in a self-sustained and intelligent way (self-healing). Of course, these
self-x abilities have to be achieved with only little additional amounts of energy
and small message overhead. Environmental and network changes during runtime, also
called dynamic events, are the main problems which have to be solved on the way
to robust self-organized networks. In most cases, networks will be deployed randomly
in unexplored regions. During runtime, undesirable movement of nodes, changes in
transmission and sensing range, random node failures, and communication interferences
may occur anywhere in the network at each point in time. In an unprepared network,
dynamic events may cause an unnecessary increase of energy consumption, a partial
network breakdown, or erroneous measurements.
In the context of this application, we target the examination and handling of dynamic
events in sensor networks using organic principles. We will classify different dynamic
events and their influence on the network. Subsequently, we will develop strategies,
which can handle dynamic events based on a set of newly designed local rules. A
simulation environment in NS-2 will help us to extract self-healing algorithms and
to check their efficiency and their impact on energy consumption. A last point of
our work package is to figure out, which emergent effects occur when applying our
new algorithms and which rules can be used to support positive or to avoid negative
emergence.
Organic Computing Middleware for Ubiquitous Environments
The project was proposed by:
Project website
Summary:
The rising complexity of smart environments in ubiquitous computing requires new
techniques for manageability. The Organic Computing (OC) Initiative identified the
self-x properties of self-organization, self-configuration, self-optimization, self-healing,
self-protection, and context-awareness as crucial for future ubiquitous embedded
systems. The proposed research project is based on the organic ubiquitous middleware
OC^t1 designed and implemented during the first two years of the DFGSPP 1183 together
with distributed Organic Managers that act locally but communicate with each other
by stress messengers piggy-backed on messages to create a globally recognizable
self-organizing behavior. The Organic Managers implement a self-configuration facility
based on cooperative social behavior, a self-optimization facility based on computer
hormones, a new failure detection algorithm and a distributed data store to support
self-healing, and a new 1 OCp. is an acronym for Organic Computing Middleware for
Ubiquitous Environments computer immunology approach for detection of malicious
messages as part of self-protection. Based on the investigations of the first two
years, the overall goals of this research proposal are:
• to redesign parts of the current OCjU middleware architecture to better separate
the middleware from the implementations of the self-x properties such that the self-x
functionalities can be implemented as services which can be plugged into the middleware
as needed,
• to investigate unicast messages instead of broadcast for information exchange
during self-configuration to better suite the minimal demands to the underlying
communication infrastructure,
• to investigate and enhance the self-healing facility by improving the failure
detection and by implementing the grouping of nodes for monitoring, and the planning
and scheduling of recovery actions,
• to extend the computer immunology approach to a full self-protection facility,
• to cooperate with the SAVE ORCA research group to investigate how to prove the
correctness of the self-configuration and self-healing capabilities, and
• to deliver an OC/i toolkit that includes all self-x facilities and can be further
used by other research projects.
Learning to Look at Humans
The project was proposed by: Dr. Rolf P. Würtz, 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.
Digital
On-Demand Computing Organism for Real-Time Systems
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), and Prof.
Brändle (neurophysiologic concepts).
Organic Fault-Tolerant Control Architecture for Robotic Applications
Prof. Dr.-Ing. Erik Maehle,
Lübeck
Institute of Computer Engineering, Universität Lübeck
together with:
Project website
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.
Organic Self-organizing Bus-based Communication Systems
Prof. Dr.-Ing. Jürgen Teich, Erlangen
Department of Computer Science 12 (Hardware-Software-Co-Design), University of Erlangen-Nuremberg, Erlangen, Germany
Project website
Summary:
Today's electronic systems comprise a multitude of complex components interacting over communication networks and bus systems.
In this project, an organic approach for the analysis, design and optimization of bus-based communication systems is investigated.
The goal of our approach is to overcome drawbacks of today's pure offline designs that are based on worst-case estimations, are
not expandable, and may easily degenerate when the environment or requirements change at run-time. In contrast, a decentralized
approach using online self-organization would be able to monitor the actual traffic of the communication system and adapt either
sending rates, probabilities, priorities, etc. accordingly.
This project intends to provide a) theoretical foundations on self-organization for bus-based communication architectures based
on game theory and utility functions, b) models as well as a design methodology including learning techniques to implement such
properties for conflicting requirements, such as deadlines and bandwidth, and c) a simulation testbed. Finally, d) a hardware
demonstrator shall be developed in order to prove the benefits of the investigated approaches in a realistic environment.
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