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Universally Programmable Intelligent Matter

Universally Programmable Intelligent Matter. A Systematic Approach to Nanotechnology Bruce MacLennan University of Tennessee, Knoxville Supported by NSF NER grant and UTK Center for Information Technology Research. Introduction & Definitions.

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Universally Programmable Intelligent Matter

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  1. Universally Programmable Intelligent Matter A Systematic Approach to Nanotechnology Bruce MacLennan University of Tennessee, Knoxville Supported by NSF NER grant and UTK Center for Information Technology Research

  2. Introduction & Definitions • Intelligent matter: individual molecules or molecular clusters function as agents • Universally programmable intelligent matter: made from a small set of building blocks that are computationally universal

  3. Goals • Avoid case-by-case nano-engineering of materials • Design one “universal material” that can be “programmed” for many applications • Requires a small, fixed set of molecular building blocks (& reactions), which can be arranged for varied purposes • Suggestive evidence for such sets

  4. K-substitution ((KX) Y) X

  5. K-Substitutionas a Molecular Process

  6. S-Substitution (with Copying) (((SX) Y) Z)  ((X Z) (Y Z))

  7. S-Substitution (with Sharing) (((SX) Y) Z)  ((X Z) (Y Z))

  8. Computational Properties • Universality: S and K can compute anything that is computable • Church-Rosser Property: substitutions can be done in any order without affecting result

  9. SK Computation • Good model of nondeterministic & parallel computing • Has been studied as model of massively parallel computer architecture • Functional computer programs can be compiled into SK networks

  10. Example: Computing a Ring Ring (X,Y) = R where rec R = Aux (X,Y,R) Aux (X, nil, R) = R Aux (x:X, y:Y, R) = (x,y) : Aux (X,Y,R)

  11. Example: Computing a Tube Tube (nil, X, Y) = Ring (X, Y) Tube (a:N, X, Y) = Ring [X, Tube(N,X,Y)]

  12. Extensions • Sensor operations respond to environmental conditions • Effector operations have physical effects on environment • Execution of these “imperative” operations must be controlled

  13. Some Static Applications • Complex physical structures: chains, tubes, spheres, fibers, networks, quasi-crystalline structures • Membranes with pores or channels • Very dense analog neural networks • Sensor & effector organs for microrobots • Conventional computation

  14. Some Dynamic Applications • Membrane with controllable channels • Free-floating clusters controlling fluid properties • Semiautonomous agents to recognize and bind molecules • Sensory transducers, such as artificial retinas & cochleas • Effectors, such as cilia & artificial muscle fibers • Self-repair

  15. Developing an Application • Write & debug program • Compile into SK network • Simulate on computer • Flatten into DNA sequence • Replicate DNA • Construct molecular network from DNA • Supply reactants for computation • Optionally, replace by permanent groups

  16. Issues • Appropriate model of computation • Replication/sharing problem • Appropriate choice of combinators • Blocking computation • Nontraditional effects on computation • Dealing with substitution error • Geometrical issues • Supply of reactants • Identifying/synthesizing appropriate reactions

  17. Current Activities • Developing mathematical model • Theoretical analysis • Developing simulation tool • Programming sample applications

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