CPSC 386: Artificial Intelligence

Professor: Ellen L. Walker

Administrative links

• Syllabus
• Revised Schedule (as of 3/16)

Resources on the Internet

• Textbook: Artificial Intelligence, A Modern Approach, by Stuart Russell and Peter Norvig
• AI on the Web, from the above textbook site.
• AI Topics, from American Association for Artificial Intelligence (AAAI)
• The Collection of Computer Science Bibliographies
• CiteSeer (Scientific Literature Digital Library) from NEC Research Institute
• CS 152: LISP
• Methods of Mathematical Proof
• CLIPS home page
  • Basic Programmers Guide
  • Link for all documentation PDF files
• GNU Prolog home page "Adventure in Prolog" -- an example and tutorial.

Class Notes

• Introduction (Chapter 1)
• Agents (Chapter 2)
• Search (Chapter 3)
• More Search, Informed Search (Chapters 3 and 4)
• Heuristics & More Search Algorithms (Chapter 4)
• Adversarial Search (Chapter 6)
• Constraint Satisfaction (Chapter 5)
• Logic (Chapters 7 and 8)
• First Order Logic & Inference (Chapters 8 and 9)
• Resolution Theorem Proving (Chapter 9)
• Knowledge Representation (Chapter 10)
• Inference Methods (Chapter 9)
  • clips: blocks.clp and block-facts.txt
  • gprolog: monkey.prolog
• Representation of Uncertainty (Chapter 13)
• Bayesian Network (Chapter 13-14)
• Learning - Decision Trees and Version Spaces (Chapter 18, 19.1)
• Learning - Ensemble Learning, EBL, ILP (Chapter 18.5, 19)
• Connectionist Systems and Neural Networks (Section 20.5)

Homework Assignments

• Warmup assignment, due 1/16/06 @ 5pm
• Homework 1, due 2/7/06 @ midnight
• Homework 2, due 3/3/06 @ midnight -- dots.lisp
• Homework 3, due 3/28/06 @ midnight

Exams

• Test 1, Feb. 9, 2006 -- covers handout from AAAI magazine, plus chapters 1, 2, 3, 4 (except 4.5), 6.1-6.4
• Test 2, Mar. 23, 2006 -- covers Chapters 7.1-7.5, 8, 9.1, 9.2 (unification only), 9.3 and Rete matching notes, 9.4, 9.5 (up to but not including "Completeness of Resolution"), 10.1-10.3 (through "Solving the Representational Frame Axiom"), 10.6 (Semantic Networks only)
• Final Exam, April 10, 2006 at 1pm -- comprehensive, covering all material from Tests 1 and 2, plus truth maintenance systems (10.8 and material from slides and class), Uncertainty and Bayesian Reasoning (Chapter 13), and the general idea of a Bayesian network (what are the nodes, what do the connections mean, what information must be stored at each node - Chapter 14.1-14.2), Learning (notes and Chapters 18.1-18.3, 19.1, 19.3 -- you are not responsible for ILP), Connectionist Systems (Section 20.5 and the notes)