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 9/2 Introduction,  "What is AI?", agents, agent architectures, and environments Skim Ch. 1, Ch. 2, Deep Blue article
Th 9/4 Introduction: PEAS agent description, environment properties
Lego Mindstorm NXT sensors and actuators		 Ch. 3, HW1 Starter Code and Documentation
Tu 9/9 Uninformed Search: breadth-first search  
Th 9/11 Uninformed Search: depth-first search, depth-limited search, tradeoffs Ch. 4.1-2
Tu 9/16 Uninformed Search:  iterative-deepening, tradeoffs Ch. 4.3-end of Ch.4
Th 9/18 Informed Search: heuristic search, best-first search, uniform cost search, greedy search, Ch. 4.1-2
Tu 9/23
Informed Search: admissibility, iterative deepening A*
Stochastic Local Search: iterative improvement		 Ch. 4.3-end of Ch.4
Th 9/25 Stochastic Local Search: simulated annealing, annealing schedules Skim Science Simulated Annealing article (given in class)
Tu 9/30 Stochastic Local Search: challenge problem: pizza orders  
Th 10/2 Machine Learning: Dynamic Programming "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", Sections 1-2 (given in class)
Tu 10/7 Machine Learning: Value Iteration "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 10/9 Machine Learning: Continuous Space Discretization, the Mountain-Car Problem  
Th 10/16 Mountain-Car Problem (cont.) Ch. 7.1-7.4 (through "A simple knowledge base"), "Clue Deduction: an introduction to satisfiability reasoning" sections 1-2
Tu 10/21 Knowledge Representation & Reasoning: Propositional Logic, syntax, semantics  
Th 10/23 Knowledge Representation & Reasoning: truth assignments, models, (un)satisfiability, validity, entailment, equivalence. Ch. 7.5, "Clue Deduction: an introduction to satisfiability reasoning" sections 3-6
Tu 10/28 Knowledge Representation & Reasoning: conjunctive normal form, resolution theorem proving, various logic problems "Clue Deduction: an introduction to satisfiability reasoning" section 8
Th 10/30 Knowledge Representation & Reasoning: Clue project knowledge base  
Tu 11/4 Knowledge Representation & Reasoning: stochastic local search for boolean satisfiability, WalkSAT, Novelty and variants Ch. 7.6 (local search algorithms)
Th 11/6 Completion of Clue project Ch. 6.1-6.2
Tu 11/11    
Th 11/13 Robotics: Configuration spaces, state-based discretization, action-based discretization Lego project documentation
     
Todd Neller