CSC384 Review Checklist

Here is a brief overview of the topics we've covered in 384 to help you organize your review of material. A couple of topics will not be covered on the exam as noted. The material you are responsible for includes: relevant chapters in the text; material from class; online lecture summaries and notes; any supplemental notes/handouts; and material from assignments.
  1. What is AI All About? (Readings: Text Ch.1, online notes)
  2. Representation and Reasoning Systems (Readings: Text Ch.2; Ch.3 excluding 3.7; online notes)
    1. Representation and Reasoning Systems and Simplifying Assumptions
    2. Definite Clause Language: Syntax (facts, rules, KBs)
    3. Definite Clause Language: Semantics (interpretations, satisfaction, models, logical consequences)
    4. Queries and Answer Sets
    5. Proof Procedures: Soundness, Completeness
    6. Bottom Up Proof Procedure
    7. Top-Down Proof Procedure (SLD-Resolution)
    8. Unifiers
    9. Uses of the Definite Clause Language
    10. Prolog
    11. Building a Knowledge Base
  3. Graph Search (Readings: Text Ch.4 excluding "Dynamic Programming" (which is in 4.6) and excluding 4.7; online notes; please take note that material on game tree search is not covered in the text, only in the online notes)
    1. Problem Solving as Graph-based Search
    2. Formalization of Graph Search
    3. Types of Solutions (shortest path, least-cost, satisficing)
    4. Generic Search Procedure (frontier, addition to frontier, selection)
    5. Search Trees
    6. Depth-first Search
    7. Breadth-first Search
    8. Least-Cost First Search
    9. Heuristics
    10. Best-first Search
    11. A* Search (admissibility, monotone restriction)
    12. Iterative Deepening
    13. Implicit Search Graphs
    14. Island-driven search
    15. Game-tree search
  4. Planning (Readings: Text Ch.8 excluding "Event Calculus", (which is in 8.2) and excluding "Partial-order Planning (which is in 8.3); you should read enough about the "Situation Calculus" (8.2) to be able to follow the text, but you are not directly responsible for material on the Situation Calculus; online notes)
    1. General Motivation for Planning
    2. World Representations: Explicit World Rep'n, Closed World Rep'n, Derived Relations
    3. Action Representations: STRIPS (preconditions, add and delete lists)
    4. Planning as Forward Search
    5. STRIPS Planning: Basic STRIPS, STRIPS with Goal Protection, Serializability
    6. Regression Planning
  5. Reasoning Under Uncertainty (Readings: Text Ch.10, excluding "Information Theory" (which is in 10.2.); online notes). You are not responsible for material on Dynamic Bayesian Networks from lecture slides.
    1. Decision Making: The need to quantify our uncertainty
    2. Basics of Probability Theory: Random Variables, Distributions, Measures (Semantics), Basic Rules
    3. Conditional Probability
    4. Probabilistic Inference: Conditioning, Computational Bottlenecks
    5. Independence and Conditional Independence
    6. What Independence Buys Us
    7. Bayesian (Belief) Networks: DAGs, CPTs, underlying semantics
    8. Constructing a Bayes net
    9. D-separation
    10. Simple Inference Schemes
    11. Variable Elimination
    12. Utility Theory: principle of Maximum Expected Utility
    13. One-shot versus Sequential Decision Problems: Decision Trees
    14. Decision Networks