KENNESAW STATE UNIVERSITY
GRADUATE COURSE PROPOSAL OR REVISION,
Cover Sheet (10/02/2002)
Course Number/Program Name CS 7015 AI & Robotics /MS-CS
Department Computer Science/ College of Science and Mathematics
Degree Title (if applicable) M.S. Computer Science
Proposed Effective Date Fall, 2012
Check one or more of the following and complete the appropriate sections:
X New Course Proposal
Course Title Change
Course Number Change
Course Credit Change
Course Prerequisite Change
Course Description Change
Sections to be Completed
II, III, IV, V, VII
I, II, III
I, II, III
I, II, III
I, II, III
I, II, III
Notes:
If proposed changes to an existing course are substantial (credit hours, title, and description), a new course with a
new number should be proposed.
A new Course Proposal (Sections II, III, IV, V, VII) is required for each new course proposed as part of a new
program. Current catalog information (Section I) is required for each existing course incorporated into the
program.
Minor changes to a course can use the simplified E-Z Course Change Form.
Submitted by:
Approved
Dr. Ken Hoganson
Faculty Member
10/1/11__
Date
Not Approved
Department Curriculum Committee Date
Approved
Approved
Approved
Approved
Approved
Approved
Not Approved
Department Chair
Date
School Curriculum Committee
Date
School Dean
Date
GPCC Chair
Date
Dean, Graduate College
Date
Not Approved
Not Approved
Not Approved
Not Approved
Not Approved
Vice President for Academic Affairs Date
Approved
Not Approved
President
Date
KENNESAW STATE UNIVERSITY
GRADUATE COURSE/CONCENTRATION/PROGRAM CHANGE
I.
Current Information (Fill in for changes)
Page Number in Current Catalog
Course Prefix and Number
Course Title
Credit Hours
Prerequisites
Description (or Current Degree Requirements)
II.
Proposed Information (Fill in for changes and new courses)
Course Prefix and Number __CS 7015______________________
Course Title __Artificial Intelligence and Robotics
________
Credit Hours 3-0-3
Prerequisites CS 6010 Advanced Algorithms and Data Structures
CS 6020 Modern Computing Systems
Description (or Proposed Degree Requirements)
This is a introduction to autonomous robotics, with a survey of Artificial Intelligence
areas of research including concepts from AI needed to provide autonomous capability to
robots. A survey of AI methods and approaches from search methods to neural networks.
A robotics kit will be included to allow students to analyze, design, build, and test simple
robotic systems.
III.
Justification
This course is part of the core requirements of the MS-Computer Science
program.
This course focuses on the learning objectives associated with developing
breadth in knowledge of computer science, with topics and overview of both
Artificial Intelligence and Robotics. The two topics are related as autonomous
robots inherently include artificial intelligence.
Computer vision and
understanding the environment is an artificial intelligence area of research that
has direct application to autonomous robots, as does understanding human
speech.
This course contributes to the following Program Objectives:
P.L.O. 1: Building on their undergraduate education in computing, students will master
advanced concepts across a targeted breadth of computer science study.
P.L.O. 5: Function effectively in teams to accomplish common goals. A number of
courses will include group/team development projects.
P.L.O. 6: Demonstrate the ability to deliver a complete development project, meeting the
standards and requirements.
IV.
Additional Information (for New Courses only)
Instructor: Ken Hoganson
Text: The Robotics Primer, Mataric, MIT press.
Prerequisites: CS 6010 Advanced Algorithms and Data Structures
CS 6020 Modern Computing Systems
Objectives:
 Students will be able to explain major concepts and techniques in the robotics field.
 Students will demonstrate how autonomous robotics differs from industrial robotics.
 Students will demonstrate the ability to construct and program an autonomous robot that
navigates a test field with specific problem solving.
 Students will be able to explain major concepts and techniques in specific fields within
Artificial Intelligence including:
 State-space searches
 Expert systems
 Genetic Algorithms
 Game playing searches with fuzzy logic
Instructional Method: The course will meet primarily for traditional lectures, which
are also recorded and streamed live to remote students. Some class meetings will
be group projects, and robotics development meetings.
Method of Evaluation: Evaluation will be through exams, quizzes, grading of lab reports, and
attendance at lab sessions. Evaluation will consist of:
Midterm Exam:
30%
Final Exam:
30%
Projects and Presentations:
40%
100%
Grading Scale:
90%+
A
80-89
B
70-79
C
60-69
D
< 60
F
V.
Resources and Funding Required (New Courses only)
Resource
Amount
Faculty
Other Personnel
Equipment
Supplies
Travel
New Books
New Journals
Other (Specify)
$0
$0
$0
$0
$0
$0
$0
$0
TOTAL
$0
Funding Required Beyond
Normal Departmental Growth
$0
VI. COURSE MASTER FORM
This form will be completed by the requesting department and will be sent to the Office of the
Registrar once the course has been approved by the Office of the President.
The form is required for all new courses.
DISCIPLINE
COURSE NUMBER
COURSE TITLE FOR LABEL
(Note: Limit 16 spaces)
CLASS-LAB-CREDIT HOURS
Approval, Effective Term
Grades Allowed (Regular or S/U)
If course used to satisfy CPC, what areas?
Learning Support Programs courses which are
required as prerequisites
Computer Science
CS 7015
AI & Robotics
3-0-3
Fall 2012
Regular
APPROVED:
________________________________________________
Vice President for Academic Affairs or Designee __
VII MS-CS Course Syllabus Template
CS 7015 Artificial Intelligence & Robotics
3 Class Hours, 0 Laboratory Hours, 3 Credit Hours
Course Description:
This is an introduction to autonomous robotics, with a survey of Artificial Intelligence areas of
research including concepts from AI needed to provide autonomous capability to robots. A
survey of AI methods and approaches from search methods to neural networks. A robotics kit
will be included to allow students to analyze, design, build, and test simple robotic systems.
Instructor: TBD
Learning Objectives:
 Students will be able to explain major concepts and techniques in the robotics field.
 Students will demonstrate how autonomous robotics differs from industrial robotics.
 Students will demonstrate the ability to construct and program an autonomous robot that
navigates a test field with specific problem solving.
 Students will be able to explain major concepts and techniques in specific fields within
Artificial Intelligence including:
 State-space searches
 Expert systems
 Genetic Algorithms
 Game playing searches with fuzzy logic
Textbook: The Robotics Primer, Mataric, MIT press.
Online Content: Instructor’s online content includes lecture notes, assignments, problem
descriptions, examples, resources, relevant articles.
Instructional Methods and Attendance Policy: The course will meet primarily for traditional
lectures, which are also recorded and streamed live to remote students. Some class meetings will
be group projects, and robotics development meetings.
Course Requirements and Assignments: Students will be required to complete examiniations,
graded projects in Artificial Intelligence, robotics design and development, make class
presentations, and complete one research paper.
Evaluation and Grading: Evaluation will be through exams, quizzes, grading of lab reports, and
attendance at lab sessions. Evaluation will consist of:
Midterm Exam:
Final Exam:
Projects and Presentations:
Grading Scale:
90%+
80-89
70-79
60-69
< 60
30%
30%
40%
100%
A
B
C
D
F
Academic Honesty: Every KSU student is responsible for upholding the provisions of the Student
Code of Conduct, as published in the Undergraduate and Graduate Catalogs. Section II of the
Student Code of Conduct addresses the University's policy on academic honesty, including
provisions regarding plagiarism and cheating, unauthorized access to University materials,
misrepresentation/falsification of University records or academic work, malicious removal, retention,
or destruction of library materials, malicious/intentional misuse of computer facilities and/or
services, and misuse of student identification cards. Incidents of alleged academic misconduct will
be handled through the established procedures of the University Judiciary Program, which includes
either an "informal" resolution by a faculty member, resulting in a grade adjustment, or a formal
hearing procedure, which may subject a student to the Code of Conduct's minimum one semester
suspension requirement.
Students are encouraged to study together and to work together on lab assignments as per the
instructor’s specifications for each assignment; however, the provisions of the STUDENT
CONDUCT REGULATIONS, II. Academic Honesty, KSC Undergraduate Catalog will be strictly
enforced in this class.
Disability policy. Kennesaw State University provides program accessibility and reasonable
accommodations for persons identified as disabled under Section 504 of the Rehabilitation Act of
1973 or the Americans with Disabilities Act of 1990. A number of services are available to help
disabled students with their academic work. In order to make arrangements for special services,
students must visit the Office of Disabled Student Support Services (770-423-6443) and arrange
an individual assistance plan. In some cases, certification of disability is required. It is the
student’s responsibility to take care of this at the beginning of the semester.
Schedule and Topic Coverage:
Week
1
2
3
4
5
6
7
8
CS 7015 AI & Robotics
(draft, subject to change)
Lecture Topic
Course intro and syllabus. Begin
overview of AI.
Projects
Students may begin
familiarization with the robot kit.
Continue familiarization with the
A.I. Problem domains. Intelligent
robot kit: begin simple mobile
Agent paradigm.
platform construction experiment.
Intro to robotics. Mobile platform
Mobile platform familiarization
stability and control considerations
experiment continues.
A.I. assignment 1:
A.I. game playing with fuzzy logic.
game/search/fuzzy logic
Robotics: actuators and motors. Fine Mobile platform familiarization
control issues.
complete.
A.I. assignment 1 due.
A.I. state space searching.
Review for Midterm
Midterm Exam
Robot construction project: design
process and specifications.
Begin robot project design
Expert System assignment
9
A.I. Expert Systems
10
11
12
13
14
15
16
A.I. Expert Systems
A.I. Genetic Algorithms
A.I. Genetic Algorithms
Robotics and A.I. Project lab time.
Robot prototype 1 demonstration
Expert system assignment due.
Genetic Algorithm assignment
Genetic Algorithm assignment
Robot prototype 2 demonstration
Genetic algorithm assignment
due.
Final robot project demonstration and evaluation against project
specifications.
Robotics and A.I. project lab time
Final Exam
Reference
Ch 1
Instructor’s online
materials
Ch 2,3,4
Instructor’s online notes
Ch 5
Instructor’s online notes
Ch 11, 12
Instructor’s online
materials and packaged
software.
Instructor’s online
materials.
Instructor’s online
materials.
Instructor’s online
materials.
Instructor’s online
specifications.
Instructor’s online
materials.
Instructor’s online
specifications.
As per Semester
Schedule