CS 371 - Introduction to Artificial Intelligence
Course Syllabus

Note: This syllabus is tentative and subject to change.  You are responsible for all material covered in lecture and assigned in readings.  Unless otherwise stated, readings are from Russell & Norvig, "Artificial Intelligence: a modern approach".
Date Topics Readings
Tu 9/3 Introduction,  "What is AI?", agents, agent architectures, and environments Ch. 2, Deep Blue article
Th 9/5 Search: search problem formulation and tradeoffs, uninformed search, breadth-first search, depth-first search, iterative-deepening Ch. 3
Tu 9/10 Search
Th 9/12 Informed Search: heuristic functions, admissibility, monotonicity, best-first search, greedy search, A*, iterative-deepening A* (IDA*), epsilon-admissibility, epsilon-admissible IDA* Sect. 4.1-4.3 (excluding SMA*)
Tu 9/17 Informed Search
Th 9/19 Advanced Search Topics: node ordering, breadth limited search, iterative-broadening search, beam search, state-based discretization, action-based discretization, iterative-refinement search Neller AAAI 2002 paper (optional)
Tu 9/24 Iterative Improvement Search: hill climbing / gradient descent, local maxima / minima, plateaux, ridges, random restarts, simulated annealing Sect. 4.4
Th 10/10 Game-tree Search: zero-sum games, utility functions, minimax Sect. 5.1-5.3
Th 10/17 Game-tree Search: alpha-beta pruning Sect. 5.4
Tu 10/22 Probabilistic Reasoning: qualification problem, probability theory, utility theory, decision theory, decision-theoretic agent, prior/unconditional probabilities, posterior/conditional probabilities, product rule, probability axioms, joint probability distribution, Bayes' rule, normalization Ch. 14
Th 10/24 Probabilistic Reasoning: belief (Bayesian) networks, noisy-OR relations, d-separation, diagnostic/causal/intercausal/mixed inference, stochastic simulation (Markov Chain Monte Carlo) Ch. 15 (except 15.3 algorithm, 15.4, and 15.6), excerpt from Pearl, Judea. "Probabilistic Reasoning in Intelligent Systems: networks of plausible inference", section 4.4.3 (skipping "Concurrent Simulation with Distributed Control")
Th 11/14 Propositional Logic: knowledge-based agent, syntax, semantics, truth tables, interpretation, entailment, proof, inference procedure, soundness, completeness, models, validity, (un)satisfiability Ch. 6
Tu 12/26 Robotics: state machines, firmware, NQC Lego robotics documentation (in lounge cabinet)

Todd Neller