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Organic Computing

A Summary of Issues Raised

During the Symposium

A Summary of Issues Raised

During the Symposiumbut also those that were left out!

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Why Organic Computing?

The Software Crisis Many software projects fail or exceed projected

costs

The algorithmic schema is doomed where too

many processes need to be

coordinated/anticipated by a programmer

Distributed processing across networks Sensor-rich processing

Strongly dynamic environmentsThere seems to be consensus that a coordinated research

initiative towards Organic Computing would be useful!

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Where Is Organic

Computing Useful?Computational processing characterized by:

High precisions is not the primary purpose (?)

Processes with some fault tolerance

Sensor-rich processing

Dynamic environments

Processes where manual decomposition is tough

E.g., HCI, computer action,perception, language,networking, complex process control

Organic computing may be less useful for Simple computer tools (e.g., text editors, graphic

programs)

Zero-fault tolerance tools (accounting, databases, etc.)

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What is Organic Computing?

Computing with a hierarchy/nesting ofactivemodules

Dynamics of module formation and growth

Search in module space

Teaching of OC modules (Bootstrapping)

Communication between OC modules

Programming by teaching, biasing, and re-

using modulesThe Physics of Biology

An Evolvable Computer

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Properties of

Organic ComputingRobustness

Structural Robustness (->redundancy)

Process Robustness (->feedback loops)

Noise Rejection (->redundancy)

Generalization (->large basins of attraction)

Flexibility Stability/Plasticity dilemma

Exploration vs. Exploitation

Modularity

Hierarchies

Adaptation, Learning, Self-Organization

Templates & Instantiation

Specialization

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OC = Modularity-Research?Modularity is good!

Reduces search space (data hiding, dimensionality reduction),allows re-use of modules, allows later global optimization

Communication between Modules Embodiment can serve a communication device

Processing within a module: Some form of self-organization

What are modules?

Inputs, outputs, reinforcement, internal state (memory) Specific computational abilities

Confidence (probabilistic modules?)

Module Competition vs. Restructuring of Modules Module selection (based on confidence, reinforcement,

self-consistency, inter-module consistency?)

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Formalizing the OC Module

OC Module

Inputs afferent

efferent

output from

other modules

confidence values

values may be grouped

Outputs transformed values

confidences

Reinforcement(Environment)

Computational

Specialization Instantiation

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Self-OrganizationWithin Modules: Comp. Specialization:

Unsupervised learning Feature extraction, density estimation

Dimensionality reduction

Self-consistency: recognition-generation networks (statisticalor functional inversion)

(Self-)Supervised Learning Nonlinear correlations->dimensionality reduction

Reinforcement Learning

Somehow we need to encode a goal

Dynamical Systems (e.g., feedback loops?)

Between Modules Contribution to the goal

Consistency with other modules

With who to interact?

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OC-Related Computing AreasStatistical Learning

Reinforcement learning (in particular modular RL)

(Self-)supervised learning

Unsupervised learning

Committee machines

Genetic/Evolutionary Programming

Artificial Life

Soft-Computation, Natural ComputationDistributed Computing

Agents community

Computer networking

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Research Approaches

Towards Organic ComputingMethods of Self-Organization & Self-Consistency

Within the module

Theory of Self-organization of Modules Communication(Interaction) between modules

Propagation of probabilities across modules?

Selection

Task-oriented Experiments Manual creation of OC modules and their interaction

Exploration of principles and constraints

Generalization to other tasks

Compare systems programmed with different paradigms

Model experimental data