CS 420/594: Advanced Topics in Machine Intelligence
Fall 2008: Biologically-Inspired Computation
Office: 217 Claxton Complex
Office Hours: 2:30–4:00 WF, or make
Office: Claxton 122C
Office Hours: TR 2:00–4:00
Classes: 1:25–2:15 MWF in Claxton 205
of Handouts, Labs, etc.
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.
the Fall semester of 2007 the topic for my CS 420/594 will be biologically-inspired
including recent developments in computational methods inspired by
nature, such as neural networks, genetic algorithms and other
evolutionary computation systems, ant swarm
optimization, artificial immune systems, swarm intelligence, cellular
automata, and multi-agent systems.
Fundamental to the understanding and implementation of
distributed computational systems is an investigation of the behavior
self-organization of a variety of systems in which useful work emerges
interaction of many simple agents. The question we address
is: How should a multitude 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 changing needs,
Fortunately, nature provides many models from which we can
In this course we will discuss natural computational 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.
You will learn about 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. These techniques are also
used in computer games and computer animation.
the goal of this goal of this course is for you to gain an intuitive
understanding of adaptive and self-organizing computational systems,
the lectures make extensive use of videos, simulations, and other
computer demonstrations. Your grade will be based on three or
four moderate-sized projects, and I will consider group projects (but
you will have to do more!). There are no other
assignments or tests. (In the past, students who
did all the work received A or B+ in this course.)
CS 594 “Biologically-Inspired Computation” is approved for the
Graduate Minor in Computational Science (IGMCS).
This is a project-oriented course and therefore
all students will be expected to have basic programming
However, for non-EECS students (e.g., those in biology, ecology,
psychology, etc.) I will provide alternate non-programming
assignments. If you have any questions about whether you
take this course, please send
Your grade will be based
on four or five projects, in which you will conduct and
write up experiments using the software associated with
book, as well as conducting experiments with software that you program
yourself. (Non-EECS students can do alternative,
There will be no exams or other homework.
Students taking CS 594 (i.e. the course for graduate credit) will be
expected to do specified additional work.
In the past, most students have earned A or B+ in this course.
If you meet all the requirements of a project, you will
get a B on it. Higher grades (B+, A–, A) are awarded for
exemplary work. If you do not meet the project requirements,
your project is late, you will get a grade lower than B on that project,
CS 420 & 594: Flake, Gary William.
The Computational Beauty of Nature. MIT
Press, 1998. See also the book’s online
webpage (including software).
Evolving List of Topics
Chapter numbers refer to Flake unless otherwise specified.
for each lecture will be posted in the course of the
(Slides from the Fall
2004 and Fall 2003
versions of the course
are still available on their websites.) Note: An “*”
indicates that the slides were revised after class.
We will do about one topic every week or so.
- Overview: course description, definition of
biologically-inspired computing, why it is important
Part 1 slides: 6/page
(1.7 MB) or 1/page
- Cellular Automata: Wolfram’s classification, Langton’s
lambda, CA models in nature, excitable media (ch. 15)
Part 2A (Cellular Automata) slides: 6/page (1.2 MB) or
animation (3 MB).
Part 2B (Slime Mold) slides*: 6/page
(1 MB) or 1/page
Part 2C (Excitable Media) slides*: 6/page (0.6 MB) or
1/page (1.6 MB).
Part 2D (Pattern Formation) slides**: 6/page (1 MB) or
1/page (4.6 MB).
- Natural and Analog Computation: artificial neural nets,
associative memory, Hebbian learning, Hopfield networks (ch. 18)
Part 3A (Hopfield Network) slides: 6/page (2 MB) or 1/page (4.2 MB).
Part 3B (Stochastic Neural Networks) slides: 6/page (600 KB) or
1/page (1.3 MB).
- Neural Networks and Learning: pattern classification
separability, single- and multilayer perceptrons, backpropagation,
internal representation (ch. 22)
Part 4A (Real Neurons) slides: 6/page
(1.3 MB) or 1/page
Part 4B (Neural Network Learning) slides**: 6/page (1.8 MB) or 1/page (2.4 MB).
- Genetics and Evolution: biological adaptation &
evolution, genetic algorithms, schema theorem (ch. 20)
Part 5A (Thermodynamics & Evolution) slides: 6/page (560 MB) or 1/page (1.1 MB).
Part 5B (Genetic Algorithms) slides: 6/page (1.3 MB) or 1/page (1.4 MB).
Online genetic algorithm demonstrations (open in new window):
- Autonomous Agents and Self-Organization: termites, ants, flocks, herds, and schools (ch. 16)
Part 6A (Nest Building) slides: 6/page (4.2 MB) or 1/page (4.8 MB).
Part 6B (Ants) slides: 6/page (0.7 MB) or 1/page (1 MB).
on Lattice Swarm simulations and source for simulator.
Some links on synchronized fireflies in the Smoky Mountains:
- The above site seems to have gone offline, but the
files can also be found here.
Links on flocking & schooling behavior:
Yuhui Shi’s page on
Particle Swarm Optimization (including demonstration).
and Cooperation: zero- and nonzero-sum games, iterated prisoner’s
dilemma, stable strategies, ecological & spatial models (ch. 17)
Part 7 (Competition and Cooperation) slides: 6/page (2.8 MB) or 1/page (2.5 MB).
Some useful links:
- Complex Systems & Phase Transitions: summary (ch.
- Adaptation: summary (ch. 23)
As time permits:
- Nonlinear Dynamics in Simple Maps (ch. 10)
- Strange Attractors (ch. 11)
- Producer-Consumer Dynamics (ch. 12)
- Controlling Chaos (ch. 13)
- Chaos, Randomness, and Computability (ch. 14)
- Project 1 Handout [pdf]
and Experimental Record [doc].
Due Sept. 12!
(Graduate students taking the Qualifying Exam will have until
Sept. 15.) See also Kristy’s website for useful
information on this project.
- Project 2 Handout [pdf].
Due Oct. 8! See also Kristy’s website for useful
information on this project.
- Project 3 Handout [html]. Due Oct. 27! See also Kristy’s website for additional information, hints, tips, etc.
- Project 4 Handout [pdf]. Due Nov. 14! See also Kristy’s website for additional information, datasets, etc.
- Project 5 Handout [pdf]. Due Dec. 3! See also Kristy’s website for additional information, datasets, etc.
- CBN Programs
You can run most of these as applets from the Website
for Flake’s textbook. You can also download sources
and executable from there. Some of these are already
available in the experiments/CBN
- NetLogo Programs
You are supposed to
be able to run NetLogo programs as Java applets. To do this,
click on their
name below. However, they don’t seem to run on all browsers
(see information with programs). Also beware that if you are
running NetLogo over a slow connection, it will have to download the
NetLogoLite jar (1.9MB). If you have downloaded a NetLogo
also download the programs (.nlogo files) directly from the NetLogo
- CA 1D
General Totalistic — 1D totalistic CA simulator (good for
Reaction — Belousov-Zhabotinsky reaction (equivalent to
Phase Plane — phase-plane display of excitable
— spiral aggregation of slime molds
— streaming aggregation of slime molds
— activation-inhibition cellular automaton for pattern
— uses small and large neighborhoods for activation-inhibition
— pattern formation by diffusing activation/inhibition
— model of segmentation (somitogenesis) in embryological development
— Hopfield network simulation
- TaskAssignment — Hopfield network for task assignment problem
- Perceptron-Geometry — demonstration of geometry of perceptron learning algorithm
- Perceptron — demonstration of perceptron learning
- Artificial-Neural-Net — demonstration of back-propagation artificial-neural-net learning
— simulation of Resnick “turmites”
— simulation of Deneubourg model of pillar construction by
- The following are NetLogo 3.1.5 programs. If you
want to run these on your own computer, you will have to download the
NetLogo 3.1.5 system, which is not the latest version. Soon I
will put the NetLogo 4.0 versions of these programs on the website,
which will run under the latest version of NetLogo.
— Conway’s classic Game of Life
— Langton’s Vants (virtual ants)
— Langton’s Vants on a large field (may take too much memory)
— generalized vants, with a programmable rule
— simulation of Resnick ants
— Camazine’s firefly synchonization model
— Wilensky’s firefly synchonization model
— Huth & Wissel model of fish schooling
— implementation Reynold’s “boids” flocking model
- PSO —
demonstration of particle swarm optimization
— ecological simulation of Iterated Prisoner’s Dilemma
— spatial simulation of Iterated Prisoner’s Dilemma
MacLennan’s home page
mail to Bruce MacLennan / MacLennan@eecs.utk.edu
This page in www.cs.utk.edu/~mclennan/Classes/420
Last updated: 2008-11-23.