Course Information and Syllabus
Version 14 of 3/25/2010
*** Subject to Change ***
Course Schedule: TTh 12:30-1:50, ZHS 352
Course Format: Primarily lecture with questions and discussion
Syllabus: http://cs.usc.edu/~rosenblo/Spring-10-CS561-Syllabus-PR.htm
Course Announcements,
Copies of Lectures, etc.: On USCÕs Blackboard site
Textbook: Artificial Intelligence: A Modern Approach (3rd Edition), by Russell, S. J. & Norvig, P. Prentice Hall, 2009
Instructor: Prof. Paul Rosenbloom
Office: SAL 238
Email: Rosenbloom@usc.edu
Phone: (213) 740-4780
Office Hours: TTh 2:00-3:00
Teaching Assistant: Hyokyeong Lee
Office: SAL 229
Email: hyokyeol@usc.edu
Phone: (213) 740-4521
Office Hours: W 10:00-12:00
Course Overview:
Artificial Intelligence (AI) seeks to understand the mechanisms underlying thought and intelligent behavior, and their embodiment in machines. This course approaches AI by using Intelligent Agents as an integrating perspective on the key topics in intelligent behavior.
Planned topics:
Introduction
1/12: Overview and Background (Chapter 1)
1/14: Intelligent Agents (Chapter 2)
Problem Solving
1/19,21,26: Problem Solving and Search (Chapter 3)
1/28: Beyond Classical Search (Chapter 4 [except 4.2])
2/2: Adversarial (Game) Search (Chapter 5)
[2/4-2/23: Programming Project 1]
Reasoning
2/4,9: Logical Agents (Chapter 7)
2/11: First-Order Logic (Chapter 8)
[2/11, 2-2:50, ZHS
159: Review session for Midterm 1]
[2/16: Midterm 1]
2/18,23: Inference in First-Order Logic (Chapter 9)
Knowledge
2/25,3/2: Knowledge Representation (Chapter 12)
Planning
3/4,9: Planning (Chapter 10)
Uncertain Knowledge
and Reasoning
3/11,23: Uncertainty (Chapter 13)
3/23,25: Probabilistic Reasoning (Chapter 14, but can skip 14.5-14.6)
3/30: Review session for Midterm 2
4/1: Guest Lecture:
Prof. William Swartout on Building Virtual Humans: a
Decade of Research
[4/1-4/26: Programming Project 2]
[4/6: Midterm 2]
Learning
4/8: Learning from Exemplars (Chapter 18)
4/13: Knowledge in Learning (Chapter 19)
4/15: Guest Lecture:
Prof. Kenji Sagae on Learning in Natural Language
Processing
4/20: Learning Probabilistic Models (Chapter 20)
Wrap Up
4/22: Philosophical Issues and Intelligent Agents Reprise and Future (Chapter 26, 27)
4/27: Guest Lecture (Video): Prof. Allen Newell (CMU) on Desires and Diversions
4/29: Review
[5/12: Final, 2-4pm]
Grading Policy:
Grades will be based on: programming projects (10% for the first and 20% for the second, for a total of 30%), midterms (20% each, for a total of 40%), and a final (30%). The midterms and final will be open book and notes, but must – along with the programming projects – reflect just the work of the individual student, with no outside help (except for questions asked of the instructor and TA). This explicitly includes no collaboration on the programming projects or use of any code from elsewhere (including the Internet) without explicit permission from the instructor or TA. The standard penalty for violating this policy is an F in the course. No make up exams will be given.
You will be allowed a total of two late days that can be used on the programming projects. This means that, without a penalty, project one can be two days late OR project two can be two days late OR each project can be one day late. Once you have used up your two late days, one additional day late will result in a 25% reduction in the total score, two additional days late will yield a 50% reduction, and no credit will be give for three or more additional days late.
Prerequisites:
Ability to program in C++, including knowledge of major data structures.
Statement for Students with Disabilities:
Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to me (or to the TA) as early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m.–5:00 p.m., Monday through Friday. The phone number for DSP is (213) 740-0776.
Statement on Academic Integrity:
USC seeks to maintain an optimal learning environment. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect oneÕs own academic work from misuse by others as well as to avoid using anotherÕs work as oneÕs own. All students are expected to understand and abide by these principles. Scampus, the Student Guidebook, contains the Student Conduct Code in Section 11.00, while the recommended sanctions are located in Appendix A: http://www.usc.edu/dept/publications/SCAMPUS/gov/. Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review, should there be any suspicion of academic dishonesty. The Review process can be found at: http://www.usc.edu/student-affairs/SJACS/.