chemical computing peter dittrich bio systems analysis department. of mathematics and computer...

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Chemical Computing Peter Dittrich Bio Systems Analysis Department. of Mathematics and Computer Science Friedrich-Schiller-University Jena Friedrich-Schiller-Universität Jena Jena Centre for Bioinformatics BMBF Grant No. 0312704A

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Chemical Computing

Peter Dittrich

Bio Systems AnalysisDepartment. of Mathematics and Computer Science

Friedrich-Schiller-University Jena

Friedrich-Schiller-Universität Jena Jena Centre for Bioinformatics

BMBF Grant No. 0312704A

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Jena Downtown ...

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Here we are ...

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Bio Systems Analysis Group

Jena Centre for Bioinformatics

CS, Jena University

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Chemical Computing

1. Computing helps Chemistry

2. Chemistry helps Computing

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What is Chemistry?

• Deals with– Substances composed of molecules– Reactions that transform substances, such

that the composition of molecules changes– Dynamics

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Where does Chemical Computing Occur in Nature?

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Principles of Chemical Computing

• Pattern recognition

• Formation of (spatial) structures

• Change of conformation

• Optical activity

• Chemical kinetics

• Energy minimization

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Chemistry Helps Computing

1. Real chemical computing

2. Artificial chemical computing

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Examples Where the Chemical Metaphor is Used in Computing

• Real Chemical Computing(Liberman 1972, 1979), (Conrad 1972) (Seelig & Rössler 1972) and others

– Enzymes– DNA/RNA-Comp – Optical– Reaction-Diffusion– Programmed Self-Assembly

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Examples Where the Chemical Metaphor is Used in Computing

Artificial Chemical Computing• Abstract Molecular Machine (Liang)• Rewriting systems

(e.g., GAMMA, CHAM, P-Systgems, ARMS, …)• Hormone systems in distributed robot control systems (e.g.

COG)• Chemical-like systems to control the behavior and emotions in

artificial agents (e.g. Creatures or PSI (D. Dörner)• Control of morpho-genetic systems (control of artificial gene

expression and morphogenesis)• Control of growth of artificial neural networks (e.g., Astor/

Adami)• Control of amorphous computers• Communication among neurons in an ANN where neurons

have spatial coordinates (e.g., neural gas by P. Husbands)

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Example for Microscopic Chemical Computing

DNA Computing (Adleman)

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Example for Macroscopic Chemical Computing Chemical Neuron

[see Hjelmfelt, Weinberger, Ross 1991]

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Example for Macroscopic Chemical

Computing: Simple Hyper-cyclic Associative Memory

[Dittrich 1995]

Hypercycle of replicating catalysts

Query

(Input)

Answer

(Output)

Some interesting aspects …

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Fine Grained Parallelism

Usually:• Distributed• Robust • Asynchronous• Emergent • Self-organizing

→ soft computing, organic computing, computational intelligence

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“Invisible Networks”

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“Invisible Networks”

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“Invisible Networks”

• A network larger than the neural network of the human brain:

• M = {2, 3, …, 10E30}

• A + B + X -> A + B + C with

C = A/B if A mod B = 0, C = C otherwise.

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Self-modification

• Self-modification(s. higher-order & generative programming)

• Strange loop

• Dualism of – structure and function – data and program– Tape and machine

Challenges

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Challenges

• Efficiency

• Scalability

• Programmability

• Adaptability

The talks in the chemical computing session

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Wolfgang BanzhafUniversity of Newfoundland

Evolving Artificial Chemistries by Genetic Programming

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Andrew AdamatzkyUniversity of the West of England

Programming Reaction- Diffusion Computers

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Tetsuya AsaiGraduate School of Informaton Science and Technology, Sapporo

Reaction Diffusion Processors

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Klaus-Peter ZaunerUniversity of Southampton

From Prescriptive Programming of Solid-State Devices to Orchestrated Self-Organization of Informed Matter

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Winfried KurthUniversity of Cottbus

Relational Growth Grammars

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Yann RadenacIRISA, Rennes

High-order Chemical Programming Style

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Questions for discussion

• How to program a chemical computer (whatever it is)?• How do chemical computing paradigms scale up?• Can the chemical metaphor lead to new computational

systems with abilities superior to conventional approaches?

• Or even to systems that can not be realized by conventional approaches?

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Thank You

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COG (MIT, Brooks)

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Growing Artificial NNs

[J. S. Astor, Christophs Adami: A Developmental Model for the Evolution of Artificial Neural Networks., Artificial Life 6(3), 189-218, 2000 http://norgev.alife.org/]

[Astor/Adami]

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PSI (D. Dörner)

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PSI (D. Dörner)