CS 420/594: Advanced Topics in Machine Intelligence

Fall 2003: Complex Systems and Self-Organization

Bruce MacLennan
Phone: 974-5067
Office: 217 Claxton Complex
Hours: 2:00-3:30 MW, or make an appointment
Email: maclennan@cs.utk.edu

Teaching Assistant:
Junlong Zhao
Phone: 974-3842
Office:  110I  Claxton Complex
Hours: 1:00-2:30 MW, or make an appointment
Email: zhao@cs.utk.edu

Classes: 3:40-4:55 MW in Cl 205

Directory of Handouts, Labs, etc.

Directory of Software, including Unix programs from CBN

This page: http://www.cs.utk.edu/~mclennan/Classes/420



CS 420 covers advanced topics in machine intelligence with an emphasis on faculty research; CS 594 is similarly focused on faculty research. In the Fall semester of 2003 the topic for my CS 420/594 will be complex systems and self-organization.

In emergent computation, information processing emerges from the parallel interaction of large numbers of comparatively simple computational units. Emergent computation is increasingly important as we seek to increase the power and robustness of computational systems by increased use of parallelism and by the exploitation of innovative computational technologies (optical, molecular, biological, etc.). Fundamental to the theory and implementation of emergent computation systems is an understanding of the behavior and self-organization of complex systems: systems in which the interaction of the components is not simply reducible to the properties of the components. This project-oriented course will focus on natural and artificial complex systems, including neural networks, cellular automata, multi-agent systems, and evolutionary systems.


To be determined, but I anticipate the following:

 Students taking the couse for graduate credit should have basic calculus through differential equations (e.g. Mat 231, 241), linear algebra (e.g. Mat 251), probability and statistics (e.g. Mat 323). Note! Take these prerequisites seriously! You will need these skills to understand the material, to do the homework and to do the projects.


I currently anticipate that your grade will be based on  a series  of projects, in which you will conduct and write up experiments using the software  associated with Flake's book, as well as conducting experiments with software that you program yourself.

Students taking CS 594 (i.e. the course for graduate credit) will be expected to do specified additional work.

Subject to change!


CS 420 & 594:  Flake, Gary William.  The Computational Beauty of Nature.  MIT Press, 1998.  See also the book's online webpage (including software).

CS 594:  Bar-Yam, Yaneer.  Dynamics of Complex Systems.  Perseus, 1997. This book is available online in pdf format.

Tentative List of Topics

(Chapter numbers refer to Flake unless otherwise specified.  Note: html version of slides may have glitches when viewed with Unix Netscape, but it's readable.)
  1. Overview: course description, the complex systems field, complex systems, emergence, complexity, methods
    Lectures 1 [pdf, html], 2 [pdf, html]
  2. Cellular Automata: Wolfram's classification, Langton's lambda, CA models in nature (ch. 15)
    Lectures 3 [pdf, html], 4 [pdf, html], 5 [pdf, html], 6 [pdf, html]
  3. Autonomous Agents and Self-Organization: termites, ants, flocks, herds, and schools (ch. 16)
    Lectures 7 [pdf, html], 8 [pdf, html], 9 [pdf, html], 10 [pdf, html], 11 [pdf, html], 12 [pdf, html], 13 [pdf, html], 14 [pdf, html]
    Some links on synchronized fireflies in the Smoky Mountains:
    Links on flocking & schooling behavior:
  4. Competition and Cooperation: zero- and nonzero-sum games, iterated prisoner's dilemma, stable strategies (ch. 17)
    Lectures 15 [pdf, html], 16 [pdf, html], 17 [pdf, html]
    Some useful links

  5. Natural and Analog Computation: artificial neural nets, associative memory, Hebbian learning, Hopfield networks (ch. 18)
    Lectures 18 [pdf (89 MB), html], 19 [pdf, html], 20 [pdf, html], 21 [pdf, html], 22 [pdf, html]
  6. Complex Systems & Phase Transitions: summary (ch. 19)
  7. Genetics and Evolution: biological adaptation & evolution, genetic algorithms, schema theorem (ch. 20)
    Lectures 23 [pdf, html], 24 [pdf, html], 25 [pdf, html]
  8. Neural Networks and Learning: pattern classification & linear separability, single- and multilayer perceptrons, backpropagation, internal representation (ch. 22)
    Lectures 26 [pdf, html], 27 [pdf, html]
  9. Adaptation: summary (ch. 23)
    Lecture 28 [pdf, html]
  10. As time permits:

  11. Nonlinear Dynamics in Simple Maps (ch. 10)
  12. Strange Attractors (ch. 11)
  13. Producer-Consumer Dynamics (ch. 12)
  14. Controlling Chaos (ch. 13)
  15. Chaos, Randomness, and Computability (ch. 14)
We will do about one topic every week or so. 



Online Resources

Return to MacLennan's home page
Send mail to Bruce MacLennan / MacLennan@cs.utk.edu

Last updated:  December 1, 2003