Download CS7075sy_FA_2016 - Kennesaw State University

CS7075 – Artificial Intelligence and Robotics (Fall 2016)
KSU has an Honor Code and a procedure relating to when academic misconduct is alleged.
All students should be aware of them. Information about the Honor Code and the misconduct
procedure may be found at the KSU website.
Course Hour: M, W 8:00 – 9:15PM J-201
Course Credit: 3.0
Optional recitation section: TBD
Instructor: Chih-Cheng Hung
Office & Hours: J384, M & W 3 – 5 PM (or by
appointment)
Phone: (678) 915-3574 (o) & E-mail:
[email protected]
Notes are posted in:
(\\CSEDATA\Faculty_Data)(p:)\Hung\CS7075
Textbook:
Russel S., Norvig P.: Artificical Intelligence, A
Modern Approach, (3rd Ed), Prentice Hall, 2010.
References:
1. Artificial Intelligence Illuminated by Ben Coppin,
Jones and Bartlett, 2004, ISBN 0-7637-3230-3.
2. Paul Graham, ANSI Common Lisp, Prentice Hall.
3. Peter Seibel, Practical Common Lisp, APress.
4. George F. Luger and William A. Stubblefield,
Artificial Intelligence (and the design of Expert
Systems) The Benjamin/Cummings Publishing, 1989.
Course Description
This is a survey course covering topics in Artificial
Intelligence and Autonomous Robotics. A survey of
AI methods and approaches from search methods to
neural networks will include hands-on with expert
systems. A robotics kit will be included to allow
students to analyze, design, build, and test simple
robotic systems running autonomously.
Course Objective:
The primary objective of this course is to provide an
introduction to the basic principles and applications
of Artificial Intelligence. Programming assignments
are used to help clarify basic concepts. The emphasis
of the course is on teaching the fundamentals, and not
on providing a mastery of specific commercially
available
software
tools
or
programming
environments. In short, this course is about the design
and implementation of intelligent agents---software or
hardware entities that perform useful tasks with some
degree of autonomy. Upon successful completion of
the course, students will have an understanding of the
basic areas of artificial intelligence including problem
solving, knowledge representation, reasoning,
decision making, planning, perception and action, and
learning -- and their applications (e.g., data mining,
information retrieval). Students will also be able to
design and implement key components of intelligent
agents of moderate complexity in Java and/or Lisp or
Prolog and evaluate their performance. Graduate
students are expected to develop familiarity with
current research problems, research methods, and the
research literature in AI.
Expectations
In addition to reading, homework, exams, and
programming assignments, this course is primarily a
lecture course. This fact, coupled with meeting only
twice per week—in the evening, no less—presents
special challenges. You can expect me to do my part
to make the class clear, interesting, interactive, and
even fun. You can expect me to be honest and
straightforward. You can expect me not to bamboozle
you or insult your intelligence. You will hear me say
“I don't agree” as well as “I don't know.” In the latter
case, I will find out. In return, I expect you to arrive
in class prepared, alert, and willing to participate.
None of us wants to spend the next fifteen weeks’
evenings listening to me drone on while you scribble
notes and try to stay awake. I expect you to ask
questions, make observations, and interact with the
lecture and the material. My goal is for you to learn a
lot and have a good time doing it.
Course Outcome:
1. Students should demonstrate the understanding of
basic concepts in artificial intelligence, including
problem
solving,
knowledge
representation,
reasoning, decision making, planning, perception and
action, and learning -- and their applications
2. Demonstrate understanding of a variety of artificial
intelligence techniques for design and implement key
components of intelligent agents.
3. Be capable of implementing different algorithms
mentioned above (2) with Lisp/Prolog/C/C++ or Java.
4. Demonstrate the knowledge of applications of
artificial intelligence.
Course Prerequisites:
CS6020
Grading Policy:
Students are guaranteed to receive the letter grade
based on the scales shown below. However, the
instructor reserves the right to modify the grading
scale so as to improve the letter grade if warranted by
the circumstances (e.g., unusually high level of
difficulty of problem sets).
Grades
will
be
based
on
one
test,
assignments/programming labs, research project (two
students maximum each group), and oral presentation.
Class Attendence and participation -and Quizzes
Assignments/Programming Labs
-Submission of assignments on due dates
Test I
-Covers all the class lectures
Test II
-Covers all the class lectures
Test III
-Selected class lectures
Term/Research Project
-Completion and submission of project/oral
presentation




necessary. An “I” grade will automatically turn into
an “F” grade, if it is not removed during the next
semester. Finally, the new grade will not be higher
than the daily grade earned by the student.
Makeup tests will be given only for nonacademic
circumstances beyond the student’s control and with
prior arrangements.
10%
Assignments:
15%
15%
15%
20%
25%
90 – 100 A
80 – 89 B
70 – 79 C
Under 70 D
Grade “I” may be assigned only when a portion of
work remains uncompleted at the final stages of the
semester due to an emergency.
For such an
exceptional situation, documentary evidence is
Assignments will include both written exercises and
programming labs. The programming labs will be
intended to build upon concepts covered in class. All
assignments must be submitted on time in order to
receive maximum credit. There is a late penalty of
10% of the grade per day up to a maximum of 4 days
from the specified due date. Programs that are turned
in later than 4 days after the due date will be assigned
zero credit. Rare exceptions to this policy might be
made, at the discretion of the course staff, under
demonstrably extenuating circumstances.
Other Comments:
Class attendance and participation are expected.
However, in the event of an absence, it is the
responsibility of the student to apprise him/herself of
any information missed. Academic dishonesty cannot
be tolerated. YOU MUST DO YOUR OWN
WORK. CREDIT CANNOT BE EARNED FOR
WORK THAT IS NOT YOUR OWN.
CS7075 Tentative Weekly Schedule (Road map and subject to change):
Week #
Topic
1
2
Introductory concepts
Intelligent Agents
3
Intelligent Agents
Solving Problems by Searching
Informed Search Methods
4
5
Classical Search
LISP
6
8
9
10
Adversial Search (Game
Playing)
Due Date to submit your
research topic (9/12/2016)
Test I – Sep. 19 and 21
Adversial Search (Game
Playing)
Logical Agent
Spring Break
Machine Learning
11
12
7
13
14
15
16
17
PPT
CS7075-01
CS7075-01
CS7075-02
CS7075-02
CS7075-03
CS7075-04
Chapters in
Textbook
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 4
CS7075-04
CS7075-05-01
CS7075-05-02
CS7075-05-03
Chapter 4
LISP Notes
CS7075-05-03
Chapter 5
CS7075-06-01
CS7075-06-01
CS7075-06-01
Chapter 7
Notes
Notes
Machine Learning
CS7075-06-02
Notes
First-Order Logic
CS7075-06-02
Chapter 8
Test II – Oct. 24 and 26
Two-layer ANN
Multi layers ANN
Test 2
Genetic Algorithms and
Evolutionary Algorithms
Natural Language Processing
AI: Present and Future
Project report due and
presentation
Nature-Inspired Algorithms
Test III (Dec. 5)
Chapter 5
Programming
Assignments
Homework
Program #1
Reading
article #1
Reading
article #2
Program #2
Program #3
Reading
article #3,
#4 and #5
Program #4
Program #5
CS7075-09
CS7075-10
ANN Notes
ANN Notes
Program #6
CS7075-14
Not in
textbook
Program #7
CS7075-15
Chapter 22
CS7075-16
Not in
textbook
Project Guidlines for CS7075
Project Guidlines:
1) Research projects should consist of the
following components:
1) Background information
2) Define the problem to be solved
3) Brief Literature Reviews
4) Algorithms and Applications
5) Implementation
6) Advantages and Drawbacks
7) Summary (New applications and
discoveries)
2) Some examples of research projects:
1) Rules and Expert Systems
2) Ontology Systems
3) Data Retrieval
4) Data Mining
5) Semantic Web: build semantic description of
websites using a probabilistic extension to
OWL + applying distributed reasoning
algorithms
6) Mapping people’s location in Siebel Center
using cameras, knowledge, and inference
7) Ant Colony Optimization model
8) Particle Swarm Optimization model
9) Genetic Algorithms
10) Differential Evolution Algorithms
11) Artificial Immune Systems
12) Artificial Neural Networks
13) Quantum Computing
14) DNA Computing
15) Bee Algorithms
16) Firefly Algorithms
17) Intelligent Planning
3) Have ideas (abstracts) approved by the
instructor as soon as possible
Policy on Academic Honesty
Students enrolled in Computer Science courses at
KSU are expected to maintain the highest standards
of academic integrity. Cases of cheating that go
undetected and hence unpunished skew the grading
curve in a class, thereby lowering the grades for
students who do not cheat. Students who cheat rob
themselves not only of knowledge and skills that they
should have acquired in a course, but also of the
experience of learning how to learn, arguably the
most valuable benefit of a university education. The
reputation of the department, the university, and the
value of the degree suffer if employers find the
graduates of a program lacking in abilities that
successful completion specific courses should
guarantee. Most professions, including Computer
Science, have codes of ethics or standards to which
individuals are expected to abide by. At the
University you practice the integrity that you must
demonstrate later.
Suspected cases of academic misconduct will be
pursued fully in accordance with KSU policies which
require that all suspected cases of academic
miscoduct be reported to the dean of students. Any
student found responsible for academic misconduct
will receive a failing grade (F) in the course (even if
the student chooses to drop the course). The dean of
students may impose additional sactions (ranging
from a disciplinary repremand to expulsion from the
university). You are strongly urged to consult the
university's policy on academic dishonesty.
The information included here is intended to help
students avoid unintentionally committing academic
dishonesty. The primary purpose of assignments is to
clarify and enhance the understanding of the concepts
covered in the lectures. Past experience with this
course has shown that this is helped by increased
interaction among students. Discussion of general
concepts and questions concerning the homework and
laboratory
assignments
among
students
is
encouraged. However, each student is expected to
work on the solutions individually.
Acknowledgements
The academic honesty policy has been compiled
using material adapted from several sources including
the past offerings of this course and other computer
science courses at other universities.
Notes
Artificial Intelligence (AI) is a broad and diverse area
of research and practice, and an introductory course
can only touch briefly on the basic ideas and
techniques of the field. CS7075 is designed to
provide you with a basic background in the
fundamentals of AI, whether you are planning to
pursue further study in AI or taking the course simply
to broaden your academic background. The
prerequisite for this course should be Data Structures
and Algorithms. In addition, we should be able to
write programs using the Prolog/Lisp/Java/C/C++
programming languages, so some amount of
programming experience will be greatly beneficial.
There are many resources available on the Web to
help you gain confidence in this very expressive (and
fun!) programming language.