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COMP 456 — Artificial Intelligence

Logic, search, and learning — hand-traceable and exam-ready

A full classical-AI course: propositional and predicate logic, unification and resolution, state-space search (BFS/DFS), heuristic and adversarial search (A*, minimax with alpha-beta), production systems and inference, knowledge representation and expert systems, reasoning under uncertainty, and machine learning from perceptrons to backpropagation. Type the Prolog/Lisp idioms, run the algorithms in Python, and learn to hand-trace every one.

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What AI Is + How to Study This Course

14 cards
  1. What is AI? Definitions, the two pillars, the Turing test 10 min ◈ 5
  2. A brief history: from Aristotle to GOFAI to agents 11 min ◈ 5
  3. Expert systems & AI applications (and their limits) 10 min ◈ 4

Logic Foundations: Propositional & Predicate Calculus

28 cards
  1. Propositional calculus: symbols, connectives, well-formed formulas 9 min ◈ 4
  2. Truth tables, equivalence, and the logical laws 12 min ◈ 5
  3. From propositional to predicate calculus: terms, predicates, quantifiers 11 min ◈ 4
  4. Semantics: interpretation, model, satisfiable, valid 10 min ◈ 3
  5. Inference rules: modus ponens, modus tollens, instantiation; sound & complete 11 min ◈ 4
  6. Unification, substitution sets, and the most general unifier 12 min ◈ 4
  7. Clause form & Skolemization (bridge to resolution) 9 min ◈ 4

Programming AI: Prolog (and Lisp idioms)

29 cards
  1. Facts, rules, queries, and the closed-world assumption 10 min ◈ 5
  2. Recursion & backtracking: the Prolog execution model 11 min ◈ 5
  3. List processing: [H|T], member, append, and recursion over lists 11 min ◈ 6
  4. Prolog for graph search: paths and cycle checking 11 min ◈ 4
  5. Collecting solutions: findall, setof, and minimum-cost paths 11 min ◈ 4
  6. Lisp idioms: s-expressions, car/cdr/cons, and recursion 9 min ◈ 5

State-Space Search: Graphs, FSMs & BFS/DFS

22 cards
  1. Graphs & finite-state machines for problems 10 min ◈ 3
  2. The state-space model [N, A, S, GD] 10 min ◈ 2
  3. Data-driven vs goal-driven search 9 min ◈ 2
  4. Breadth-first search (FIFO queue) 11 min ◈ 4
  5. Depth-first search & iterative deepening 11 min ◈ 5
  6. Backtracking & and/or graphs 10 min ◈ 2
  7. Branch-and-bound & reasoning as search 11 min ◈ 4

Heuristic & Adversarial Search

21 cards
  1. Why heuristics? Hill-climbing and the local-maximum trap 11 min ◈ 4
  2. Best-first search and f(n) = g(n) + h(n) 11 min ◈ 3
  3. A* and admissibility 12 min ◈ 3
  4. Heuristic quality: monotonicity, informedness, and the 8-puzzle 12 min ◈ 3
  5. Minimax for two-player games 11 min ◈ 3
  6. Alpha-beta pruning 12 min ◈ 3
  7. Beam search & the informedness/cost trade-off 9 min ◈ 2

Production Systems, Control & Inference

17 cards
  1. Production systems: rules, working memory, recognize–act 11 min ◈ 3
  2. Data-driven (forward) vs goal-driven (backward) chaining 10 min ◈ 2
  3. Forward chaining: deriving facts to a fixpoint 11 min ◈ 3
  4. Backward chaining & DFS inference trees over rules 12 min ◈ 3
  5. Pattern-directed search & the knowledge/control split 10 min ◈ 3
  6. The blackboard architecture 10 min ◈ 3

Knowledge Representation & Expert Systems

22 cards
  1. Semantic networks & inheritance 10 min ◈ 3
  2. Frames & scripts 11 min ◈ 4
  3. Conceptual dependency & conceptual graphs 10 min ◈ 3
  4. Expert systems: architecture & the five modules 11 min ◈ 3
  5. Rule-based vs case-based vs model-based reasoning 12 min ◈ 3
  6. Case-based reasoning in depth 11 min ◈ 3
  7. Planning: STRIPS & the frame problem 12 min ◈ 3

Reasoning Under Uncertainty

23 cards
  1. Probability foundations: counting & conditional probability 11 min ◈ 4
  2. Bayes’ theorem, MAP, and naive Bayes 11 min ◈ 4
  3. Certainty factors (Stanford / MYCIN) 10 min ◈ 3
  4. Nonmonotonic reasoning & truth maintenance 11 min ◈ 5
  5. Fuzzy sets & Dempster-Shafer (breadth) 9 min ◈ 3
  6. Bayesian belief networks (breadth) 12 min ◈ 4

Machine Learning

34 cards
  1. What is learning? Induction, generalization, and inductive bias 11 min ◈ 4
  2. Version space & the candidate-elimination algorithm 11 min ◈ 3
  3. Decision trees & ID3 (information gain) 12 min ◈ 3
  4. Explanation-based learning & analogy (breadth) 9 min ◈ 3
  5. The artificial neuron & the perceptron 12 min ◈ 4
  6. Backpropagation in multilayer networks 12 min ◈ 5
  7. Competitive & associative networks (breadth) 9 min ◈ 3
  8. Genetic algorithms 11 min ◈ 5
  9. Probabilistic ML & the credit-assignment theme (breadth) 10 min ◈ 4

Automated Reasoning, NLP & AI as Empirical Enquiry

26 cards
  1. Resolution refutation: proving theorems by contradiction 12 min ◈ 6
  2. Horn clauses & the Prolog interpreter 10 min ◈ 4
  3. Weak methods: the Logic Theorist, GPS & means-ends analysis 10 min ◈ 3
  4. Natural language understanding: the pipeline & CFG parsing 11 min ◈ 4
  5. Stochastic NLP: POS tagging, n-grams & PCFGs 9 min ◈ 4
  6. AI as empirical enquiry: PSSH, the paradigms & the generalization problem 11 min ◈ 5

Exam Prep: Hand-Tracing & Past-Paper Drills

25 cards
  1. Logic & truth-table drills 11 min ◈ 4
  2. Search & heuristic drills 11 min ◈ 5
  3. Inference & production-system drills 12 min ◈ 5
  4. Prolog programming drills 12 min ◈ 6
  5. ML & short-answer drills 12 min ◈ 5
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