CSC411 General Information

Overview and definitions of Artificial Intelligence (AI). Search, including depth-first and breadth-first techniques with backtracking. Knowledge representation with emphasis on logical methods, Horn databases, resolution, quantification, unification, skolemization and control issues; non-monotonic reasoning; frames; semantic nets. AI systems, including planning, learning, natural language and expert systems. An AI programming language may be taught at the instructor's discretion.

Course: CSC411 Artificial Intelligence, Spring, 2013 
Moodle page: http://moodle.wolfware.ncsu.edu/course/view.php?id=33936
Time/location: Monday/Wednesday, 3:50 - 5:05, EB III 2201 
Instructor: Robert St. Amant (stamant@csc.ncsu.edu)
Office hours: Monday/Tuesday, EB II 2268, by appointment (just send email)
Teaching assistant: Matthew Adams (mbadams3@ncsu.edu) or Srinath Ravindran (sravind2@ncsu.edu)
TA office hours: Thursday, 3:00 - 4:00, EB II 2246
Textbook: Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig. Prentice Hall, 2009 (3rd edition)
Final exam: Wednesday, May 1, 1:00 - 4:00, EB III 2201.

Tentative Schedule

Note: I'll include links to external Web sites for readings in the schedule below. Occasionally external Web sites change in a way that breaks a link. If this happens, let me know and I'll fix the link. If you need a more immediate solution, a Google Scholar search for the title and author of a reading should work.

Date Presentation
Jan 7 Introduction
  • Final notes
  • Introduction (notes)
  • Textbook: Chapter 1
  • Reading: Turing, A. M. (1950). Computing Machinery and Intelligence, Mind 49: 433-460. [Cogprints link]
  • Reading: Hauser, L. (2005). Chinese Room Argument, Internet Encyclopedia of Philosophy. [Web page]
  • Reading: Dennett, D. (1991). Consciousness Explained, Penguin, 1991 [Wikipedia page]
Jan 9 Agents and Environments
  • Final notes
  • PEAS (notes)
  • Agents (notes)
  • Textbook: Chapter 2
  • Reading: Etzioni, O. & Weld, D. (1994), A Softbot-Based Interface to the Internet, Communications of the ACM 37 (7), 72-76. [UW link]
Jan 13 Homework #1 due at 11:45 PM. (Sample solution.)
Jan 14 Agents, continued
Search
  • Problem solving (notes)
  • Uninformed search (notes)
  • Informed search (notes)
  • Textbook: Problem solving: Chapter 3, sections 3.1 to 3.3; Uninformed search: Chapter 3, section 3.4 (minus subsection 3.4.6); Informed search: Chapter 3, section 3.5 (subsections 3.5.1 and 3.5.2) and section 3.6 (subsection 3.6.1 and 3.6.2)
  • Reading: Newell, A. (1982). The Knowledge Level, Artificial Intelligence 18 (1), 87-127. [link]
Jan 16 Search, continued
Jan 22 Homework #2 due at 11:45 PM. (Sample solution.)
Jan 23 Search, continued
Jan 28 Search, continued
Jan 30 Optimization
  • Textbook: Chapter 4, Section 4.1.
  • Wrap-up of informed search.
  • Optimization (notes)
Feb 3 Homework #3 due at 11:45 PM. (Sample solution.)
Feb 4 Optimization, continued
Feb 6 Constraints
  • Textbook: Chapter 6
  • Constraints (notes)
Feb 10 Constraints, continued
Feb 12 Homework #4 due at 11:45 PM. (Sample solution.)
Feb 13 Logic
  • Textbook: Chapter 7.
  • Propositional logic (notes)
  • Peter Suber's Translation Tips, on translating between English and logical notation
    • http://legacy.earlham.edu/~peters/courses/log/transtip.htm
Feb 18 Logic, continued
Feb 19 Homework #5 due at 11:45 PM. (Sample solution.)
Feb 20 Logic, continued
Feb 27 Midterm review (Sample midterm)
Mar 11 First-Order Logic
  • Textbook: Chapter 8
  • FOL (notes)
Mar 13 First-Order Logic, continued
Mar 18 Midterm exam (Solution)
Mar 20 Planning
  • Textbook: Chapter 10.
  • Planning (notes)
Mar 25 Planning, continued
  • Planning: HSP (notes)
  • Planning: Graphplan (notes)
Apr 2 Homework #6/7 due at 11:45 PM. (Sample solution.)
Apr 3 Planning, continued
  • Planning: other approaches (notes)
Apr 2 Homework #8 due at 11:45 PM. (Sample solution.)
Apr 8 Uncertainty
  • Textbook: Chapter 13
  • Uncertainty (notes)
Apr 10 Uncertainty, continued
Apr 15 Probabilistic reasoning
  • Textbook: Chapter 14, Sections 14.1 and 14.2
  • Bayes (notes)
Apr 17 Machine learning
  • Textbook: Chapter 18
  • Decision trees (notes)
Apr 22 Machine learning, continued
  • Regression (notes)
    		•
    April 24 Final exam review (Sample final exam)
    Apr 24 Proposals for Homework #10 (optional) due at 11:45 PM.
    Apr 26 Homework #9 due at 11:45 PM. (Sample solution.)
    May 1 Final examination, 1:00 PM.
    May 5 Homework 10 (optional) due at 11:45 PM.