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EECE 216 Syllabus |
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Contact Information
Course Description and Prerequisites
This course provides a broad exposure to the field of artificial intelligence.
Topics include intelligent agents, search, game playing, propositional
logic and first-order predicate calculus, knowledge bases, planning,
uncertainty, learning, communication and perception, and philosophical
foundations.
Prerequisites: COEN 030 or COSC148.
Course Goals
- By the end of this course, you should...
- have a broad, general understanding of many of the areas that comprise
the field of artificial intelligence (AI).
- be able to employ some of the methods from several areas within
AI.
- be able to begin to understand and critique the AI literature.
Course Objectives
- By the end of this course, you should...
- Introduction
- be able to evaluate the various definitions of AI.
- be able to summarize the history of AI.
- Intelligent agents
- be able to provide a definition of a rational agent.
- be able to compare and contrast various agents including reflex,
model-based, goal-based, and utility-based agents.
- be able to classify the environment in which a particular agent
operates.
- Search
- be able to formulate a search problem.
- be able to evaluate a search algorithm on the basis of completeness,
optimality, time complexity, and space complexity.
- be able to explain and contrast uniformed and informed searches.
- be able to compare, contrast, classify, and implement various
search algorithms including breadth-first, uniform-cost, depth-first,
depth-limited, iterative deepening, bidirectional, best-first,
greedy, A*, RBFS, SMA*, hill-climbing, simulated annealing, and
genetic algorithm searches.
- Game playing
- be able to provide a definition of a game.
- be able to evaluate, compare, and implement the minmax and alpha-beta
algorithms, including for games of chance.
- be able to describe the current state-of-the-art game playing
programs.
- Logics
- be able to describe the characteristics of a knowledge-based
agent including its knowledge base and inference mechanism.
- be able to compare and contrast propositional logic and first-order
predicate calculus.
- be able to employ propositional logic and first-order predicate
calculus to design a knowledge-based agent.
- be able to analyze and employ logical inference in first-order
logic.
- Planning
- be able to describe the basic representation for planning.
- be able to explain the partial order planning algorithm.
- Uncertainty
- be able to describe different types of uncertainty including
degree of belief and degree of truth.
- be able to explain various probability constructs including
prior probability, conditional probability, probability axioms,
probability distributions, and joint probability distributions.
- be able to apply Bayes' rule.
- be able to apply belief networks to model a problem with uncertainty.
- be able to briefly describe other approaches to modeling uncertainty
such as Dempster-Shafer theory and fuzzy sets/logic.
- be able to explain the utility theory axioms and the maximum
expected utility principle.
- be able to assess various utility functions including multi-attribute
utility functions.
- be able to design a decision network.
- be able to explain information value theory.
- be able to define a sequential decision problem.
- be able to explain the main ideas of game theory.
- Learning
- be able to describe a learning agent.
- be able to apply and analyze decision trees to learning problems.
- be able to determine the information gain provided by an attribute
in a decision tree.
- be able to summarize computational learning theory.
- Natural language processing
- be able to describe the component steps of communication.
- be able to contrast formal and natural languages in the context
of grammar, parsing, and semantics.
- Philosophical foundations
- be able to explain the incompleteness theorem and the halting
problem and the limitations they describe for AI.
- be able to argue various philosophical positions for and against
AI.
Course Outline
| What |
When |
- I: Artificial Intelligence
- 1 Introduction
- 2 Intelligent Agents
|
wk1-2 |
- II: Problem-Solving
- 3 Solving Problems By Searching
- 4 Informed Search Methods
|
wk3 |
- 6 Adversarial Search (Game Playing)
|
wk4 |
- III: Knowledge And Reasoning
- 7 Logical Agents
|
wk5 |
- 8 First-Order Logic
- 9 Inference In First-Order Logic
|
wk6-7 |
- IV: Acting Logically
- 11 Planning
|
wk8 |
- V: Uncertain Knowledge And Reasoning
- 13 Uncertainty
- 14 Probabilistic Reasoning
|
wk9-10 |
- 16 Making Simple Decisions
- 17 Making Complex Decisions
|
wk11-12 |
- VI: Learning
- 18 Learning From Observations
|
wk13 |
- VII: Communicating, Perceiving, And Acting
- 22 Agents That Communicate
|
wk14 |
- VIII: Conclusions
- 26 Philosophical Foundations
- 27 AI: Present And Future
|
wk15 |
Calendar
| Week |
Month |
Tuesday |
Thursday |
| 1 |
August |
30 |
- 1
|
| 2 |
September |
- 6
- AIMA Software Assignment due
|
- 9
-
|
| 3 |
September |
13 |
- 15
-
|
| 4 |
September |
20
- Article Review 1 due
- Homework 1 due
|
- 22
|
| 5 |
September |
27
Review Critique 1 due |
- 29
|
| 6 |
October |
4 |
- 6
-
|
| 7 |
October |
- 11
- Article Review 2 due
- Homework 2 due
|
- 13
|
| 8 |
October |
- 18
- Review Critique 2 due
- Midterm Exam
|
- 20
|
| 9 |
October |
- 25
- Homework 3 due
|
- 27
|
| 10 |
November |
1
Article Review 3 due |
- 3
|
| 11 |
November |
- 8
- Review Critique 3 due
|
- 10
|
| 12 |
November |
- 15
- Homework 4 due
|
- 17
|
| 13 |
November |
22 |
- 24
-
|
| 14 |
November |
- 29
- Article Review 4 due
|
- 1
-
|
| 15 |
December |
6 |
- 8
- Review Critique 4 due
- Homework 5 due
|
| |
December |
- 13
- Final Exam 3:30-5:30
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-
|
NOTE: All dates and numbers are subject to change as deemed necessary!
November 18, 2005 is the final day for withdrawal with grade of W.
Course Materials
Required Texts
Grading
| What |
Number |
Value per |
Total |
| Article Reviews |
4 |
20 |
80 |
| Review Critiques |
4 |
5 |
20 |
- Homeworks
|
5 |
80 |
400 |
- Midterm
|
1 |
200 |
200 |
| Final |
1 |
300 |
300 |
| Total |
|
|
1000 |
NOTE: All dates and numbers are subject to change as deemed necessary!
Grade Scale
| 93+ |
A |
| 89-93 |
AB |
| 85-89 |
B |
| 81-85 |
BC |
| 77-81 |
C |
| 74-77 |
CD |
| 70-74 |
D |
The grading scale is the most stringent one you will be held to, i.e.
I can give you a higher letter grade than shown on the scale, but never
a lower one.
Late Assignments
I will deduct 25% for assignments up to one day late, 50% for two days
late, and 75% for up to three days late. I will not accept work later
than 3 days. The weekend will count as 1 day. Assignments are due at
the beginning of class. They are late after that.
Attendance and Participation
I have always enjoyed teaching classes where the students actively
participate - a conversation is more fun than a monologue! Participation
is expected, lack of attendance and participation will result in a lower
grade.
Assignments
You should expect to spend, on average, from twelve (12) to fifteen
(15) hours on article reviews, critiques, reading, homeworks, and other
preparation for this class. This time is in addition to the three (3)
hours of lecture you are expected to attend every week.
Homework assignments are due at the beginning of class. You will need
to turn in the assignments via two methods. Both are required. The first
is to turn a hardcopy of the assignment at the beginning of class. If
there are multiple pages they must be stapled. The second is to use
D2L's Dropbox.
All assignments are due according to the the times specified in the
calendar.
Article Reviews
There will be four (4) article reviews. This will help you understand
the relevant literature for your research project. You will identify
articles that are relevant to the course topic, if you are unsure of
its relevance double check with the instructor. For each article you
will write a 1 page summary and a 1 page critique.
Peer Review of Article Reviews
Critiquing others work is an excellent mechanism for improving your
own. It is backbone of the peer review process. You will be expected
to evaluating others work including critiquing others article reviews.
Homeworks
There will be five (5) homeworks, which will be collected and graded.
The homework assignments will be scaled to 400 points. The homeworks
will be combinations of questions from the book and programming problems.
You must cite, using IEEE format, your references including online ones.
Exams
There will be a midterm exam worth 200 points. There will be a final
exam worth 300 points.
Dishonesty
My Policy
ACADEMIC DISHONESTY OF ANY FORM WILL NOT BE TOLERATED IN THIS CLASS.
ANY STUDENT FOUND TO BE PERFORMING ANY ACT OF ACADEMIC DISHONESTY WILL
BE SUBJECT TO THE MAXIMUM PENALTY FOR THE PARTICULAR OFFENSE.
I will not tolerate any form of dishonesty in any of my classes and
I hope you feel the same. If you become aware of any form of dishonesty
taking place in any activity concerned with any of your classes it is
your duty to make sure that the offense is made known to the proper
authority. This is a problem which affects all of us and I am asking
for your help in keeping the standards of education here at Marquette
University as high as they deserve to be.
Viruses
Any assignment turned in in electronic format that contains a virus
will receive a zero.
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