CS 420/594: Advanced Topics in Machine Intelligence

Fall 2004: 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

Yifan Tang
Phone: 974-8990
Office:  124 Claxton Complex
Hours: 5:00-6:00 W, or make an appointment
Email: ytang@cs.utk.edu

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

Directory of Handouts, Labs, etc.
(currently empty)

Class listserv (cs594compsys) archives.

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 2004 the topic for my CS 420/594 will be complex systems and self-organization.

Fundamental to the theory and implementation of massively parallel, distributed 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.  How should systems of millions of independent computational (or robotic) agents cooperate in order to process information and achieve their goals, in a way that is efficient, self-optimizing, adaptive, and robust in the face of damage or attack? 

Fortunately, nature provides many models from which we can learn.  In this course we will discuss natural systems that solve some of the same problems that we want to solve, including adaptive path minimization by ants, wasp and termite nest building, army ant raiding, fish schooling and bird flocking, pattern formation in animal coats, coordinated cooperation in slime molds, synchronized firefly flashing, soft constraint satisfaction in spin glasses, evolution by natural selection, game theory and the evolution of cooperation, computation at the edge of chaos, and information processing in the brain.

We will also investigate specific computational applications of these ideas, including artificial neural networks, simulated annealing, cellular automata, ant colony optimization, artificial immune systems, particle swarm optimization, and genetic algorithms and other evolutionary computation systems.
This is a project-oriented course and the lectures make extensive use of simulations and other computer demonstrations.


This is a project-oriented course and therefore all students will be expected to have basic programming skills.

 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.


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, including some mathematical analysis.

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.  Slides from the Fall 2003 version of the course are still available on its website.  Note: An "*" after the lecture number indicates that the slides were revised after class.)
  1. Overview: course description, the complex systems field, complex systems, emergence, complexity, methods
    Lectures: 1*, 2*.

  2. Cellular Automata: Wolfram's classification, Langton's lambda, CA models in nature, excitable media (ch. 15)
    Lectures: 3*, 4*, 5, 6, 7*.

  3. Autonomous Agents and Self-Organization: termites, ants, flocks, herds, and schools (ch. 16)
    Lectures: 8*, 9*, 10*, 11*, 12, 13, 14, 15*, 16.

    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, ecological & spatial models (ch. 17)
    Lectures: 17*, 18*, 19*.

    Some useful links:

  5. Natural and Analog Computation: artificial neural nets, associative memory, Hebbian learning, Hopfield networks (ch. 18)
    Lectures: 20 [1.4 MB], 21* [1 MB], 22*, 23* [1.5 MB], 24*, 25.

    A useful link:

  6. Complex Systems & Phase Transitions: summary (ch. 19)

  7. Genetics and Evolution: biological adaptation & evolution, genetic algorithms, schema theorem (ch. 20)
    Lectures: 26, 27.

  8. Neural Networks and Learning: pattern classification & linear separability, single- and multilayer perceptrons, backpropagation, internal representation (ch. 22)
    Lectures: 28, 29*.

  9. Adaptation: summary (ch. 23)

    As time permits:

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



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Last updated:  2004-11-29.