This document provides information about the Fall 1997 course "EEL 5840: Elements of Machine Intelligence" at the University of Florida. The course will provide an in-depth look at machine learning theories and their implementation on real and simulated robots. Students will complete homework assignments applying machine learning concepts and a semester-long project implementing a learning algorithm on a real or simulated robot. The course will have exams, homework, and a project, and aims to further students' understanding of both classical and modern machine learning approaches and their limitations.

1. University of Florida
Department of Electrical and Computer Engineering
Fall 1997
EEL 5840: ELEMENTS OF MACHINE INTELLIGENCE
Instructors: Antonio A. Arroyo and Keith L. Doty
Class Web Site: http://www.mil.ufl.edu/eel5840
Text Readings from the literature and class notes provided during the semester.
General Objectives
Provide an in-depth look at Machine Learning, both classical and modern, with a view toward grounding the
concepts in physical reality; to implement Machine Learning Algorithms in autonomous robots and contrast with
simulations of intelligent autonomous robots; to provide an "engineering approach" to the emerging field of
Machine Intelligence (MI); to impart a conceptual foundation on the principles behind current MI technology; to
provide greater understanding of the limitations of current learning theories.
Course Objectives
You will read a diverse technical literature on learning and intelligence and will attempt to apply different theories
and paradigms to physical autonomous robots, or to simulations of autonomous robots, in your homework
assignments. You will contrast your simulation studies with physical embodied studies, ascertaining the value and
limitations of each approach. In your project you will focus on a particular machine learning technique in depth and
develop an implementation on a real robot or a simulation. You will write a formal report on your project. You will
also present and demonstrate your project to the class.
Project
Your project should realize a particular machine learning theory on a real robot. The theory may be an existing one,
a modified existing theory, or one you have invented yourself.
You may also write a robot simulator to illustrate learning theories. However, you will need to work with someone
with a robot and try to apply your simulation results on a real machine. A number of simulators already exist in this
area. If you choose to simulate, you should explore what has already been done.
You may work in teams of two on a project, but project responsibility must be clearly delineated.
Robots for Homework and Projects
If you took EEL5666, you can use the robot you built in that course to satisfy homework and project requirements.
You can also purchase a robot kit or an assembled robot.
Grading
There will be two exams.
Midterm Exam 30%
Final Exam 30%
Homework and Project 40%
Total 100%
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2. EEL 5840 Schedule
HOMEWORK ASSIGNMENTS: FALL SEMESTER 1997
WEEK HOME SET DUE READING
1. 8/25 None Classical AI and Machine Intelligence
2. 9/1 9/5 Classifiers
Holiday 9/1
3. 9/8 Home Set 1 9/12 Neural Net Perspective
4. 9/15 Home Set 2 9/19 Psychological Perspective on Learning and Intelligence:
Conditioned Response, Habituation…
5. 9/22 Home Set 3 9/26 Adaptive Behavior and Ethology
6. 9/29 Home Set 4 10/3 Reactive Behavior: Case Study
7. 10/6 Home Set 5 10/10 Behavior with Memory : Case Study
8. 10/13 Exam 1 10/17 Biology based neural control: Neurophysiology
9. 10/20 Home Set 6 10/24 Non-linear Dynamics Approach to Robot Behavior
10. 10/27 Home Set 7 Reinforcement Learning, Markov Decision Processes, ,
Gradient Search
11. 11/3 Homecoming 11/7-8 Dynamic Programming, Q-Learning
12. 11/10 Holiday 11/11 Q-Learning,
13. 11/17 Home Set 9 11/21 Evolutionary Learning: Genetic Algorithms
14. 11/24 Holiday 11/27-28 Complexity Theory: Order and Chaos
15. 12/1 Artificial Life
16. 12/8 Final Exam 12/15 Presentation of Projects
References
We will read from many of the references cited, as time permits. This list will be increased as the semester
progresses.
[Beer 1990] Randall Beer, Intelligence as Adaptive Behavior. Academic Press, N.Y., 1990.
[Beer, Quinn, et al 1997] R. Beer, R.D. Quinn, H.J. Chiel, and R.E. Ritzmann. Biologically Inspired Approaches to
Robotics. Communications of the ACM, Vol. 40, No. 3, March 1997, pp. 31-38.
[Braitenberg 1984] V. Braitenberg. Vehicles, Experiments in Synthetic Psychology. MIT Press, Cambridge, MA,
1984.
[Denardo 1982] E.V. Denardo, Dynamic Programming: Models and Applications. Prentice Hall, Englewood Cliffs,
N.J.
[Gage 1993] Douglas W. Gage. Randomized Search Strategies with Imperfect Sensors. Proceedings of SPIE Mobile
Robots VIII, Vol. 2058, Boston, Sept.9-10, 1993, pp. 270-279.
[Gleick 1987] J. Gleick, Chaos: Making a New Science. N.Y., Viking, 1987.
[Goldberg 1989] D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-
Wesley, Reading, MA., 1989.
[Holland 1975] J.H. Holland, Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann
Arbor, 1975.
[Kaufman 1993] S. Kaufman, The Origins of Order: Self-Organization and Selection in Evolution. N.Y., Oxford
University Press, 1993.
[Lorenz 1973] K. Lorenz. Foundations of Ethology. Springer-Verlag, N.Y.,1973.
[Maes 1991] P. Maes, editor. Designing Autonomous Agents. MIT Press, Cambridge, MA, 1991.
[Mataric` 1994] M.J. Mataric`. Interaction and Intelligent Behavior. Ph.D. Thesis, Dept. of EECS, MIT, Cambridge,
MA, 1994.
[Moffat and Frijda 1994] David Moffat and Nico H. Frijda. Where there’s a Will there’s an Agent. ECAI-94
Workshop on Agent Theories, Architectures & Languages. Springer-Verlag, Amsterdam, The Netherlands, Aug.
1994, pp. 244-260.
[Resnick 1994] M. Resnick. Turtles, Termites, and Traffic Jams. MIT Press, Cambridge, MA., 1994.
[Reynolds 1987] Craig W. Reynolds. Flocks, Herds, and Schools: A Distributed Behavioral Model. Proceedings of
SIGGRAPH ’87 (Computer Graphics 21(4), July1987, edited by Maureen C. Stone, pp. 25-34).
[Sutton 1988] Richard S. Sutton. Learning to Predict by the Methods of Temporal Differences. Machine Learning,
3:9-44, Kluwer Academic Publishers, Boston, 1988.
[Walter 1950] W. G. Walter. An Imitation of Life. Scientific American, May 1950, pp. 42-45.
[Whitehead and Lin 1995] Steven D. Whitehead and Long-Ji Lin. Review of Reinforcement Learning. Elsevier
Science B.V., 1995.
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