Syllabus
General Information

Course Description:

[from the Cal Poly 2011-13 Catalog] "Programs and techniques that characterize artificial intelligence. Programming in a high level language. 3 lectures, 1 laboratory." Prerequisites: CSC 103 (Fundamentals of Computer Science III), with a grade of C- or better. Students should be familiar with programming in Java, and be able to work with elementary statements in propositional and predicate logic.

Meeting Times and Places:


Lecture Section 1: Tue/Thu, 9:40 - 11:00 am, in 14-257
Lab Section 1: Tue/Thu, 12:10 - 1:30 pm in 14-257
Lecture Section 2: Tue/Thu, 1:40 - 3:00 pm, in 14-257
Lab Section 2: Tue/Thu, 3:10 - 4:30 pm in 14-257
Instructor: Dr. Franz J. Kurfess
Office hours: TBA in class
Office: 14-218, Email fkurfess@calpoly.edu

Goals and Objectives

The goal of this course is to understand important problems, challenges, concepts and techniques from the field of Artificial Intelligence. In order to achieve this, students learn how to analyze, design, and program intelligent agents of varying complexities. These agents gather information from their environment, convert it into a suitable internal representation (which may be augmented with information provided by the designer or other sources), analyze their internal knowledge to determine suitable actions, and finally execute some actions. More specifically, after successful completion of the course, students should
  • know classical examples of artificial intelligence
  • be able to identify critical aspects of intelligence in humans and in computer-based systems
  • know characteristics of programs and systems that can be considered "intelligent"
  • be familiar with the main problem solving methods used in AI programs
  • understand the use of heuristics in search problems and games
  • know a variety of ways to represent, process and retrieve knowledge and information
  • know the fundamentals of artificial intelligence programming techniques in a modern programming language
  • consider ideas and issues associated with social technical, and ethical uses of systems that involve artificial intelligence
Textbooks
The following textbooks will be used in this course:
  • Artificial Intelligence: A Modern Approach (Third Edition) by Stuart Russell and Peter Norvig; Prentice Hall, 2009. ISBN 978-0136042594.
    It is available from the El Corral bookstore, which also has a customized edition that contains only the chapters relevant for our class here, for about half the price. You can also check     http://isbn.nu/9780136042594 for prices of new and used copies at online bookstores like Amazon, Abebooks.com, Half.com and a few others. While it is better to have the newest edition of the textbook, much of the material covered in this class is also well presented in the older editions. There are also international editions available; I assume that the contents will be the same, but I have not checked this.
  • Introduction to Artificial Intelligence by Wolfgang Ertel, Springer, 2011; ISBN 978-0857292988. This is a shorter and significantly cheaper book, written by a former colleague of mine. The German original was translated into English by Nate Black, a Cal Poly CSC alum. Structure and content are similar to Russell and Norvig’s book, but the level of detail and amount of material are significantly reduced. For our class, however, it is sufficient.
There is a column in the course schedule indicating the chapters in the book that correspond to a topic discussed in class. Students are expected to read the respective chapters before the topic is covered in class. For further reading, here are some more suggestions:
There are also a few books on more practical aspects of AI programming and intelligent agents:
Lecture Notes
The PowerPoint slides used in class, together with other auxiliary material, will be made available to students via a shared Dropbox directory.
Additional Material
Further material will be made available through handouts in class, and through pointers to relevant Web pages.
Grading Policy
The table shown gives an overview of the grading scheme I typically use for this class. I reserve the right to change the formula used. Please note that the project consists of several parts which will be evaluated separately. The project will be done in teams, and the performance of the team as a whole will be graded unless there is a clear disparity in the contribution of the individual team members. Should this be the case, I might ask for additional documentation like work sheets, email messages, or draft copies of documentation to evaluate individual contributions.





Assignments20%
Project30%
Labs20%
Exams20%
AI Nugget Presentation10%
Student scores will be made available via PolyLearn/Moodle. If you notice any discrepancies between an entry there and the score indicated on the returned work, please let me know.
Academic Dishonesty and Cheating
The expectations below are based on Cal Poly’s Code of Student Conduct.
Academic dishonesty can be a serious offense. Any instances of cheating or plagiarism may be reported to the department chair and the Office of Student Rights & Responsibilities (OSRR). The Cal Poly rules and policies are listed in the Cal Poly catalog as well as at the OSRR web site, http://www.calpoly.edu/~osrr/index.html. If the rules are unclear or you are unsure of how they apply, ask the instructor beforehand.
We may use programs or services like Moss to compare assignments within a section, across all current sections of this class, and with old assignments. While such programs are not perfect, they detect suspicious similarities even after replacement of variable names and other identifiers. In general, the use of program libraries is acceptable, but not if they provide functionality whose implementation is the purpose of the lab or assignment. If you use libraries you need to indicate this in the documentation. Unless explicitly stated, assignments and labs are to be done individually.
Turning in work is presumed to be a claim of authorship unless explicitly stated otherwise.