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, 2nd ed.".
 
Date Topics Readings
Tu 8/31 Introduction,  "What is AI?", agents, agent architectures, and environments Ch. 2, Deep Blue article
Th 9/2 Introduction: PEAS agent description, environment properties
Uninformed search: search problem formulation and tradeoffs		 Ch. 3, HW1 Starter Code and Documentation
Tu 9/7 Uninformed Search: breadth-first search, depth-first search, depth-limited search, iterative-deepening, tradeoffs  
Th 9/9 Repeated state detection, measures: time versus node count
Informed Search: heuristic search, best-first search, uniform cost search, greedy search, A* search 		Ch. 4.1-2
Tu 9/14 Recursive search implementations
Informed Search: admissibility
Stochastic Local Search: iterative improvement		 Ch. 4.3-end of Ch.4
Th 9/16 Stochastic Local Search: simulated annealing Skim Science Simulated Annealing article (given in class)
Tu 9/21 Stochastic Local Search: annealing schedules; challenge problem: optimal pizza topping selection  
Th 9/23 Stochastic Local Search: challenge problem (cont.) "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", Sections 1-2 (given in class)
Tu 9/28 Machine Learning: Dynamic Programming "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", remainder (given in class); Russell & Norvig sections 17.1-17.2
Th 9/30 Machine Learning: Value Iteration  
Th 10/7 Machine Learning: Continuous Space Discretization, the Mountain-Car Problem  
Tu 10/12 Mountain-Car Problem (cont.) Ch. 7.1-7.4 (through "A simple knowledge base"), "Clue Deduction: an introduction to satisfiability reasoning" sections 1-2
Th 10/14 Knowledge Representation & Reasoning: Propositional Logic, syntax, semantics  
Tu 10/19 Knowledge Representation & Reasoning: truth assignments, models, (un)satisfiability, validity, entailment, equivalence. Ch. 7.5, "Clue Deduction: an introduction to satisfiability reasoning" sections 3-6
Th 10/21 Knowledge Representation & Reasoning: conjunctive normal form, resolution theorem proving, various logic problems "Clue Deduction: an introduction to satisfiability reasoning" section 8
Tu 10/26 Knowledge Representation & Reasoning: Clue project knowledge base  
Th 10/28 Knowledge Representation & Reasoning: stochastic local search for boolean satisfiability, WalkSAT, Novelty and variants Ch. 7.6 (local search algorithms)
Tu 11/2 Completion of Clue project Ch. 6.1-6.2
Th 11/4 Game-Tree Search: definitions, minimax Ch. 6.3-6.4
Tu 11/9 Game-Tree Search: alpha-beta pruning, heuristic evaluation Ch 25.4
Th 11/11 Robotics: Configuration spaces, state-based discretization, action-based discretization Lego project documentation
     
Todd Neller