CS 561:  Foundations of Artificial Intelligence

Spring 2008

Course Information and Syllabus

Version 10 of 4/8/2008

 

Course Schedule: TTh 2:00-3:20, MHP 106

Course Format: Primarily lecture with questions and discussion

Syllabus: http://www-rcf.usc.edu/~rosenblo/Spring-08-CS561-Syllabus-PR.htm

Course Announcements, Copies of Lectures, etc.: On USCÕs Blackboard site

Textbook: Artificial Intelligence: A Modern Approach (2nd Edition), by Russell, S. J. & Norvig, P.  Prentice Hall, 2002

Instructor: Prof. Paul Rosenbloom

            Office: SAL 238

            Email: Rosenbloom@usc.edu

            Phone: (213) 740-4780

            Office Hours: TTh 11:00-12:00

Teaching Assistant: Nader Noori

            Office: SAL 209

            Email: nnoori@usc.edu

            Phone: (213) 740-4513

            Office Hours: MW 11:00-1:00

Grader: Anuj Gupta

            Email: anujg@usc.edu

Grader: Jayeshkumar Senjaliya

            Email: senjaliy@usc.edu

 

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 (may change a bit over time):

 

Introduction

1/15: Overview and Background (Chapter 1)

1/17: Intelligent Agents (Chapter 2)

 

Problem Solving

1/22,24: Search (Chapter 3)

1/29,31: Heuristic Search (Chapter 4)

2/5: Adversarial (Game) Search (Chapter 6)

[2/5-2/19: Programming Project 1]

 

Knowledge and Reasoning

2/7,12: Logical Agents (Chapter 7)

2/14: [No Class]

2/19: First-Order Logic (Chapter 8)

[2/26: Midterm 1]

2/21,28: Inference in First-Order Logic (Chapter 9)

3/4,3/6: Knowledge Representation (Chapter 10)

 

Planning

3/11,13: Planning (Chapter 11)

 

Uncertain Knowledge and Reasoning

3/25,27: Uncertainty (Chapter 13)

3/27,4/1: Probabilistic Reasoning (Chapter 14)

 

4/3: Guest Lecture (Video): Prof. Allen Newell (CMU) on Desires and Diversions

[4/1-4/28: Programming Project 2]

[4/8: Midterm 2]

 

Learning

4/10: Learning from Observation (Chapter 18)

4/15: Knowledge in Learning (Chapter 19)

4/17: Guest Lecture: Prof. Jonathan Gratch (Assoc. Dir. for Virtual Human Research, ICT) on Emotions in Human-Agent Interactions

4/22: Statistical Learning Methods (Chapter 20)

 

Wrap Up

4/24: Philosophical Issues and Intelligent Agents Reprise and Future (Chapter 26, 27)

4/29: Guest Lecture: Prof. Craig Knoblock (ISI) on Geospatial Information Integration

5/1: Review

[5/8: 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).  No make up exams will be given. The programming projects must be turned in on time for full credit.  One day late will result in a 25% reduction in the total score, two days late will yield a 50% reduction, and no credit will be give for three or more 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/.