CS 371 - Introduction to Artificial Intelligence
Course Syllabus |

Note: This syllabus is tentative and subject to change. Each reading assignment should be completed before the class on the date indicated. If a reading assigned in class does not match the reading assignment here, the reading assigned in class supersedes. Unless otherwise noted, readings are from Russell & Norvig, "Artificial Intelligence: a modern approach, 4th ed.".

Class | Date | Topic | Readings (parenthesized reading are optional) |

1 | Introduction, "What is AI?"; Uninformed search: problem definition | Course Information page; (Skim R&N Ch. 1, 2); R&N 3.1-3.3; Deep Blue article | |

2 | Uninformed Search: breadth-first search, depth-first search, recursive depth-first search, depth-limited search, iterative-deepening depth-first search | 3.4, HW1 Starter Code and Documentation, (Peg Solitaire Demonstration) | |

3 | Uninformed Search: repeated state detection, in-class implementation | ||

4 | Informed Search: heuristic search, best-first search, uniform cost search, greedy search | rest of Ch. 3 except 3.5.5 (memory-bounded search) | |

5 | Stochastic Local Search: hill descent with uphill step probability, simulated annealing, parameter tuning, demonstrations, challenge problem implementation | 4.1, (4.2), Skim Science Simulated Annealing article | |

6 | Stochastic Local Search: second challenge problem | ||

7 | Game-Tree Search: minimax, alpha-beta pruning | 5.1-5.3; game-tree search slides; (FYS 187-4 videos: game trees, alpha-beta pruning) | |

8 | Game-Tree Search: heuristic evaluation, in-class heuristic development | 5.4; (FYS 187-4 videos: heuristic/static evaluation, heuristic features) | |

9 | Game-Tree Search: time management | H. Baier, M. Winands. Time Management for Monte-Carlo Tree Search in Go. (skim time management strategies) | |

10 | Game-Tree Search: expectiminimax | 5.5; Poker Squares rules/play grid, Learning and Using Hand Abstraction Values for Parameterized Poker Squares, (slides); (FYS 187-4 video: chance node, expectimax, and optimal Pig play reasoning) | |

11 | Game-Tree Search: Monte Carlo Tree Search (MCTS), UCT | Section 2 through 2.3 iof L. Kocsis, C. Szepesvari. Bandit based Monte-Carlo Planning.; Sections 1 (through 1.1) and 3.1 of C. Browne et al. A Survey of Monte Carlo Tree Search Methods | |

12 | Machine Learning: Dynamic Programming | "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", Sections 1-2; (For reference in class presentation: Pig slides) | |

13 | Machine Learning: Dynamic Programming (cont.), Value Iteration | "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", remainder; Russell & Norvig sections 17.1-17.2 | |

14 | Machine Learning: Value Iteration (cont.) | ||

15 | In-class Midterm | ||

16 | Q-Learning: definitions, backup diagram, update rule, application to Pig Solitaire | section 6.5, of Reinforcement Learning: an introduction | |

17 | Q-Learning: application to Pig Solitaire (continued) | ||

18 | Q-Learning: application to Pig Solitaire (completed); Data Science and Machine Learning: TBA | TBA | |

19 | Data Science and Machine Learning: TBA | TBA | |

20 | Data Science and Machine Learning: TBA | TBA | |

21 | Data Science and Machine Learning: TBA | TBA | |

22 | Data Science and Machine Learning: TBA | TBA | |

23 | Data Science and Machine Learning: TBA | TBA | |

24 | Data Science and Machine Learning: TBA | TBA | |

25 | Probabilistic Reasoning: Bayesian Networks, Gibbs Sampling, a Markov Chain Monte Carlo (MCMC) algorithm for reasoning on Bayesian networks | 14.1-14.2, An Introduction to Monte Carlo Techniques in AI - Part II, 14.5.2, Pearl, Judea. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Revised 2nd Printing, Morgan Kaufmann, San Francisco, California, USA, 1988, Table 1 (p. 197), section 4.4.3 (pp. 210-216, up to "Justifying the Computations") | |

26 | Probabilistic Reasoning (cont.), AI and Ethics | ||

27 | Course Evaluations, AI and Ethics | ||

28 | 4th Hour Project Demos | ||

Final Exam Schedules
on Registrar page under "Resources" (Fall,
Spring)
Monday, December 9th, 1:30 PM - 4:30 PM
Final: |

Todd Neller

tneller@gettysburg.edu