I400/I590 Artificial Life as an approach to Artificial Intelligence

Fall Semester 2005


Professor: Larry Yaeger

Office: Eigenmann Hall, Rm 907

Office Hours: By appointment, any time I'm not in a meeting; see my work calendar

Phone: (812) 856-1845

Email: larryy (at) indiana.edu


Meeting Times: 4:00pm – 5:15pm Tuesdays & Thursdays                 Location: Informatics Bldg, Rm I107


Textbook (required): Braitenberg, V. Vehicles: Experiments in Synthetic Psychology

All other reading materials available online via links on this page.


NOTE: This document appears in both a public and a private place; reading material links only work on the private site, to abide by copyright restrictions.  (Lecture note links work on both sites.)  The private site’s URL is provided to enrolled students.


Fall 2005 Schedule and Reading List


Links under the “Topics” heading are to PowerPoint lecture notes.  You can download these in advance if you want something to take notes on, but be warned, they are likely to be in flux until the day of the class.


In the topics, S# means Speaker #.  (Speaker topics can be inferred from the schedule, but are listed explicitly at the bottom of the page.)

In the reading assignments, B# means chapter # of the Braitenberg book.





Reading Assignment (for next class)

Extras (not required)


TU 30 Aug

Class intro, L1-Intro to Artificial Life




TH  1 Sep

Intro to Braitenberg, L2-Is it alive?

Farmer & Belin, B1



TU  6 Sep

S1 (John Burgoon), B1, discussion




TH  8 Sep

L3-Intro to GAs

Goldberg, B2

Holland, Fraser, Charbonneau


TU 13 Sep

S2 (Matt Whitehead), B2, discussion




TH 15 Sep

L4-Simulated Evolution

Ray, Sims, B3

Sims1, Sims2, Sims3


TU 20 Sep

S3 (Josh Walgenbach), B3, discussion, test prep

Exam 1, take-home                            



TH 22 Sep

Exam 1 due, L5-Neural Networks Pt. 1 - Terms & Defs

Anderson, B4

Rumelhart & McClelland


TU 27 Sep

Return and discuss exams, S4 (Nick Gentile), B4, discussion




TH 29 Sep

L6-Neural Nets Pt. 2 - Association & Hebb

James, Hebb, B5



TU  4 Oct

Bruce Wheeler – Cultured Neural Networks




TH  6 Oct

John Beggs – Criticality in Biological & Artificial Neural Networks




TU 11 Oct

L7-Intro to Information Theory, S5? (Mike Beyer), B5?

Schneider, B6

Shannon, n-grams


TH 13 Oct

L8-Neural Nets Pt 3 – Hebbian learning via Information Theory

Linsker1, Linsker2, B7



TU 18 Oct

S7 (Giancarlo Schrementi), B6, B7, discussion, test prep




TH 20 Oct

In-class Midterm (Exam 2)                                          

Mystery reading



TU 25 Oct

Return and discuss exams, Mystery class




TH 27 Oct

L9-Evolution & Learning (Koko, Dolphin, Alex)

Hinton&Nowlan, Parisi, Chalmers, B8



TU  1 Nov

S8 (Paul McDonald), B8, Informal presentation of project ideas




TH  3 Nov

L10-Organisms simulated and real

Walter, NewSci, Salience, Giurfa, B9



TU  8 Nov

S9 (Ian Marks), B9, discussion




TH 10 Nov

L11-Intelligence as an Emergent Phenomenon

Hillis, B10



TU 15 Nov

S10 (J Duncan), B10, discussion, test prep

Exam 3, take-home



TH 17 Nov

Exam 3 due, L12-Evolution of Intelligence (movies)

Yaeger, B11, B12



TU 22 Nov

Return and discuss exams, S11 (Waimao Ke), B11, B12, discussion

B13, B14



TH 24 Nov

No Class - Thanksgiving Recess




TU 29 Nov

Olaf Sporns – Complexity of Brain Networks




TH  1 Dec

L13-Is it alive? Pt. 2, Measuring Complexity




TU  6 Dec

Projects due, S12 (Virgil Griffith), B13, B14, Project Demos




TH  8 Dec

Project Demos, Discussion and review for final exam




TH 15 Dec

Final Exam (Exam 4) 7:15 – 9:15pm, I107






Students will be expected to attend class, do weekly readings, and participate in discussions.  In addition, each student will present and lead a discussion on the material in one topic area, and will do one of the following:


Š      Write a final paper (grads 20-25 pages, undergrads 15-20 pages) demonstrating insight into one of the topic areas.

Š      Write a functioning ALife simulator of at least modest complexity (more than just a simple CA or Conway Game of Life; student may consult with teacher before writing).

Š      Turn in a technical paper describing the results of an ALife experiment, carried out with Tierra, Avida, Swarm, PolyWorld, or other ALife software (student may consult with teacher in selection of software and topic).


There will be four exams—two take-home exams, an in-class midterm, and an in-class final exam.  Students may opt to drop the lowest test grade, thus the final is optional, if a student is satisfied with the first three test results.  The midterm and particularly the final may include cumulative questions, but will primarily focus on the material since the last test.  Dates are indicated in the schedule above.


Course Structure


Generally, each week’s theme will bridge weekends, being introduced by me in the TH lecture class, and then followed up with readings before the next class on TU, in order to allow students maximum time to read and prepare for discussion.  On the following TU, student presentations of a topic related to the reading materials will take place, followed by a discussion of one of the chapters from the Braitenberg book, followed by any additional material I need to communicate, and class discussion of that week’s topic.  (There may be exceptions to this pattern, to accommodate guest speakers, holidays, etc.)


Each student will prepare one presentation during the semester on a topic related to the reading materials.  We will agree on and set dates in the first class.  If there are more than 13 students in the class, we will fit extra presentations in as needed.  These presentations are to be 15 to 20 minutes only.  In general, I hope students will select one of the papers referenced by the primary reading material and use that as the basis for their presentation.  But if there is a topic of particular interest to a student in the reading material itself and I have not covered that topic in the lecture class, that will be acceptable.  Related papers not taken from the reference list may also be acceptable, but require my approval.


Each student will also work on one final project.  As indicated above, this project may take the form of writing your own ALife simulator, performing experiments with an existing ALife simulator, or researching and writing on a topic in the field, in order to accommodate all computer skill levels.  Projects will be due on Tuesday 6 December, 2005.




Each of the four tests, class participation, the topic presentation, and the final project will contribute to the total grade as follows:



5 pts

(Yes, it really does count)

Gedanken experiment

5 pts



10 pts



20 pts





Test 1 (take-home)

20 pts


Test 2 - Midterm

20 pts


Test 3 (take-home)

20 pts


Test 4 - Final

20 pts






120 pts

(100 pts after dropping the lowest test score)


The extended gedanken experiment will be defined in the first week.  This experiment will pose a challenge that, if answered correctly by anyone in the class, anytime before the end of the semester, will result in the credit being received by the entire class.


Grades will not be curved and will be assigned as follows:












































Cooperation on the gedanken experiment is encouraged, as is discussion in general.  In fact, I intend to set up an email list to facilitate discussions outside of class.  However, tests must be taken individually, both take-home and in-class.  Cheating will be reported according to university policies.


General Course Description


Artificial Life is a broad discipline encompassing the origins, modeling, and synthesis of natural and artificial living entities and systems.  Artificial Intelligence, as a discipline, tries to model and understand intelligent systems and behavior, typically at the human level.  This class will introduce core concepts and technologies employed in Artificial Life systems that can be used to approach the evolution of Artificial Intelligence in computers.  Key themes include:


- bottom-up design and synthesis principles,

- definitions and measurements of life and intelligence,

- genetic algorithms,

- neural networks,

- the evolution of learning,

- the emergence of intelligence,

- computational ecologies, and

- information theory-based measures of complexity.


Our path through these materials will lay the theoretical groundwork for an approach to Artificial Intelligence based on the tenets and practices of Artificial Life--an approach which utilizes evolution to start small and work our way up a spectrum of intelligence, from the simplest organisms to the most complex, rather than attempting to model human-level intelligence from the outset.


Lectures and readings will be based on seminal papers and introductory texts in these fields, drawing from the Artificial Life conference proceedings, and technical papers by Donald Hebb (from which we obtain Hebbian learning), Rumelhart and McClelland (editors of and authors in the original Parallel Distributed Processing books that launched the modern neural network field), Ralph Linsker ("Infomax" theoretical approach to neural network learning), Hinton and Nowland (the "Baldwin effect"), William James ("the greatest American psychologist), W. Grey Walter, Tom Ray, Karl Sims, Danny Hillis, and others.  We will also read and discuss Braitenberg's seductive and influential Vehicles book.




Langton1:  Langton, C. G., Artificial Life, preface to Artificial Life, The Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems held September, 1987 in Los Alamos, New Mexico, Santa Fe Institute Studies in the Sciences of Complexity Proc. Vol. VI., edited by C. Langton, Addison Wesley, Redwood City, CA, 1989


Farmer & Belin:  Farmer, J. D., and A. d’A. Belin, "Artificial Life: The Coming Evolution"  In Artificial Life II, edited by C. Langton, C. Taylor, J. Farmer, and S. Rasmussen.  Proceedings of the Artificial Life II Conference (in 1990), Santa Fe Institute Studies in the Sciences of Complexity Proc. Vol. X.  Addison-Wesley, Redwood City, CA, 1992


Goldberg:  Goldberg, D. E., A Gentle Introduction to Genetic Algorithms, p. 1-23, Chapter 1 of Genetic Algorithms in Search, Optimization, and Machine Learning, by D. E. Goldberg, Addison-Wesley 1989


Ray: Ray, T. S. 1992. Evolution, ecology and optimization of digital organisms. Santa Fe Institute working paper 92-08-042


Sims:  Sims, K., Evolving Virtual Creatures, Computer Graphics, Annual Conference Series, (SIGGRAPH ‘94 Proceedings), July 1994, pp.15-22.


Anderson: Anderson, J. A., General Introduction, p. xiii-xxi, Neurcomputing, Foundations of Research, ed. by J. A. Anderson and E. Rosenfeld, A Bradford Book, MIT Press, Cambridge, Massachusetts, 1988


Rumelhart & McClelland: Rumelhart, D. E. and McClelland, J. L., PDP Models and General Issues of Cognitive Science, p. 110-146, Chapter 4 of Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Volume 1: Foundations, ed. by D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, A Bradford Book, MIT Press, Cambridge, Massachusetts, 1986


James: James, W., Association, Chapter XVI of Psychology (Briefer Course), p. 253-279, Holt, New York, 1890 (with introduction, p. 1-14 from Neurocomputing, Foundations of Research, ed. by J. A. Anderson and E. Rosenfeld, A Bradford Book, MIT Press, Cambridge, Massachusetts, 1988)


Hebb: Hebb, D. O., Introduction (p. xi-xix) and Chapter 4, "The first stage of perception: growth of the assembly" (p. 60-78), The Organization of Behavior, Wiley, New York, 1949 (with introduction, p. 43-56 from Neurocomputing, Foundations of Research, ed. by J. A. Anderson and E. Rosenfeld, A Bradford Book, MIT Press, Cambridge, Massachusetts, 1988)


Schneider:  Schneider, T., “Information Theory Primer”, <http://www.lecb.ncifcrf.gov/~toms/paper/primer/>


Linsker1: Linsker, R., "Towards an Organizing Principle for a Layered Perceptual Network" in Neural Information Processing Systems, ed. by D. Z. Anderson. American Institute of Physics, New York, 1988


Linsker2: Linsker, R., "Self-Organization in a Perceptual Network", Computer 21(3), 105-117, March 1988


Hinton & Nowlan: G. E. Hinton and S. J. Nowlan. How learning can guide evolution. Complex Systems, 1:495--502, 1987


Parisi: Parisi, D., S. Nolfi, and F. Cecconi, "Learning, Behavior, and Evolution", Tech. Rep. PCIA-91-14, Dept. of Cognitive Processes and Artificial Intelligence, Institute of Psychology, C.N.R., Rome, June 1991. (Appeared in Proceedings of ECAL-91—First European Conference on Artificial Life, December 1991, Paris; also in Varela, F, Bourgine, P. Toward a pratice of autonomous systems. MIT Press. 1991


Chalmers: Chalmers, D., "The Evolution of Learning: An Experiment in Genetic Connectionism" in Connectionist Models, Proceedings of the 1990 Summer School, edited by D. S. Touretzky, J. L. Elman, T. J. Sejnowski, G. E. Hinton, Morgan Kaufmann, San Mateo, CA, 1991


Walter: Walter, W. G. (1950), "An Imitation of Life", Scientific American, 182(5), 42-45, May 1950


NewSci: Douglas, F., “Do fruit flies dream of electric bananas?”, New Scientist, 14 February 2004


Salience: Frye, M.A. & Dickinson, M.H. “A signature of salience in the Drosophila brain”, commentary on article by Swinderen & Greenspan, Nat. Neur. 6 (6) 544-546 June 2003


Giurfa: Giurfa, M., Zhang, S., Jenett, A., Menzel, R., Mandyam, V., “The concepts of 'sameness' and 'difference' in an insect”, Nature 410(6831) 930-933, 19 Apr 2001


Hillis: Hillis, D. W., Intelligence as an Emergent Behavior, p. 175-189, Daedalus, Journal of the American Academy of Arts and Sciences, special issue on Artificial Intelligence, Winter 1988


Yaeger: Yaeger, L. S., Computational Genetics, Physiology, Metabolism, Neural Systems, Learning, Vision, and Behavior or PolyWorld: Life in a New Context, p. 263-298, Proceedings of the Artificial Life III Conference (in 1992), ed. Chris Langton, Addison-Wesley, 1994


Langton2: Langton, C. G., Computation at the Edge of Chaos: Phase Transitions and Emergent Computation, p. 12-37, Emergent Computation, Proceedings of the Ninth Annual International Conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks, Los Alamos, NM, 1989, ed. Stephanie Forrest, North Holland, 1990


Speaker Topics


S1 – Intro to Artificial Life or Is It Alive?

S2 – Genetic Algorithms

S3 – Simulated Evolution

S4 – Neural Networks, Intro

S5 – Neural Networks, Association & Hebb

S6 – Information Theory

S7 – Neural Networks, Information theoretic approach to neural network learning

S8 – Combining evolution and learning

S9 – Animal intelligence, intelligence in simulated organisms

S10 – Intelligence as an emergent phenomenon

S11 – Evolution of intelligence

S12 – Complexity