I400/I590 Artificial Life as an approach to Artificial
Intelligence
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 siteÕs URL is provided to enrolled students.
Fall 2005 Schedule and Reading List
Links under the ÒTopicsÓ 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.
|
Class |
Date |
Topics |
Reading Assignment (for next class) |
Extras (not required) |
|
1a |
TU 30
Aug |
Class intro, L1-Intro to Artificial Life |
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|
1b |
TH 1 Sep |
Intro to Braitenberg, L2-Is it alive? |
Farmer & Belin, B1 |
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2a |
TU 6 Sep |
S1 (John Burgoon), B1, discussion |
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2b |
TH 8 Sep |
Goldberg, B2 |
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|
3a |
TU 13
Sep |
S2 (Matt Whitehead), B2, discussion |
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3b |
TH 15
Sep |
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|
4a |
TU 20
Sep |
S3 (Josh Walgenbach), B3, discussion, test prep |
Exam 1, take-home |
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|
4b |
TH 22
Sep |
Exam 1 due, L5-Neural Networks Pt. 1 - Terms & Defs |
Anderson, B4 |
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5a |
TU 27
Sep |
Return and discuss exams, S4 (Nick Gentile), B4, discussion |
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5b |
TH 29
Sep |
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6a |
TU 4 Oct |
Bruce Wheeler – Cultured Neural Networks |
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6b |
TH 6 Oct |
John Beggs – Criticality in Biological & Artificial Neural Networks |
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7a |
TU 11
Oct |
L7-Intro to Information Theory, S5? (Mike Beyer), B5? |
Schneider, B6 |
|
|
7b |
TH 13
Oct |
L8-Neural Nets Pt 3 – Hebbian learning via Information Theory |
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|
8a |
TU 18
Oct |
S7 (Giancarlo Schrementi), B6, B7, discussion, test prep |
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8b |
TH 20
Oct |
In-class Midterm (Exam 2) |
Mystery reading |
|
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9a |
TU 25
Oct |
Return and discuss exams, Mystery class |
|
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|
9b |
TH 27
Oct |
Hinton&Nowlan, Parisi, Chalmers, B8 |
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10a |
TU 1 Nov |
S8 (Paul McDonald), B8, Informal presentation of project ideas |
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10b |
TH 3 Nov |
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11a |
TU 8 Nov |
S9 (Ian Marks), B9, discussion |
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11b |
TH 10
Nov |
Hillis, B10 |
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12a |
TU 15
Nov |
S10 (J Duncan), B10, discussion, test prep |
Exam 3, take-home |
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12b |
TH 17
Nov |
Exam 3 due, L12-Evolution of Intelligence (movies) |
Yaeger, B11, B12 |
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13a |
TU 22
Nov |
Return and discuss exams, S11 (Waimao Ke), B11, B12, discussion |
B13, B14 |
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TH 24
Nov |
No Class - Thanksgiving Recess |
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14a |
TU 29
Nov |
Olaf Sporns – Complexity of Brain Networks |
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14b |
TH 1 Dec |
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|
15a |
TU 6 Dec |
Projects due, S12 (Virgil Griffith), B13, B14, Project Demos |
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15b |
TH 8 Dec |
Project Demos, Discussion and review for final exam |
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TH 15
Dec |
Final Exam (Exam 4) 7:15 – 9:15pm, I107 |
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Requirements
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:
á Write a final paper (grads 20-25 pages, undergrads 15-20 pages) demonstrating insight into one of the topic areas.
á 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).
á 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 weekÕs 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 weekÕs 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.
Grading
Each of the four tests, class participation, the topic presentation, and the final project will contribute to the total grade as follows:
|
Participation |
5 pts |
(Yes, it really does count) |
|
Gedanken experiment |
5 pts |
|
|
Presentation |
10 pts |
|
|
Project |
20 pts |
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|
|
|
|
Test 1 (take-home) |
20 pts |
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Test 2 - Midterm |
20 pts |
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Test 3 (take-home) |
20 pts |
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Test 4 - Final |
20 pts |
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Total |
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:
|
A+ |
98-100 |
4.0 |
|
A |
93-97 |
4.0 |
|
A- |
90-92 |
3.7 |
|
B+ |
88-89 |
3.3 |
|
B |
83-87 |
3.0 |
|
B- |
80-82 |
2.7 |
|
C+ |
78-79 |
2.3 |
|
C |
73-77 |
2.0 |
|
C- |
70-72 |
1.7 |
|
D+ |
65-69 |
1.3 |
|
D |
55-64 |
1.0 |
|
D- |
50-54 |
0.7 |
|
F |
0-49 |
0.0 |
Conduct
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.
References
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. dÕA. 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 Ô94 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., ÒInformation Theory PrimerÓ, <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., ÒDo fruit flies dream of electric bananas?Ó, New Scientist, 14 February 2004
Salience: Frye, M.A. & Dickinson, M.H. ÒA signature of salience in the Drosophila brainÓ, 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., ÒThe concepts of 'sameness' and 'difference' in an insectÓ, 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