CSCI 377: Artificial Intelligence

LECTURE NOTES

1. Language and Semantics (Sep 4): (pdf)
2. Propositional Logic (Sep 9, 11): (pdf)
3. Propositional Inference (Sep 14, 16): (pdf)
4. Resolution (Sep 18, 21, 23): (pdf)
5. DPLL (Sep 25, 28, 30): (pdf)
6. Search Spaces (Oct 2): (pdf)
7. Uninformed Search (Oct 7, 9, 12): (pdf)
8. Heuristic Search (Oct 14): (pdf)
9. Heuristics (Oct 16): (pdf)
10. Analysis of Search (Oct 21, 23): (pdf)
11. Minimax (Oct 26): (pdf)
12. Expectimax (Oct 28): (pdf)
13. Markov Decision Processes (Nov 6, 9, 11, 13): (pdf)
14. Passive Reinforcement Learning (Nov 16): (pdf)
15. Active Reinforcement Learning (Nov 18): (pdf)
16. Bayesian Networks (Nov 30, Dec 2): (pdf)
17. Exact Inference in Bayesian Networks (Dec 4, 7): (pdf)

LECTURE VIDEOS

1. Propositional Logic (Sep 2): (video1) (video2) (video3)
2. Language and Semantics (Sep 4): (video1) (video2)
3. Propositional Logic, continued (Sep 9): (video4) (video5)
4. Propositional Logic, continued (Sep 11): (video6)
5. Propositional Inference (Sep 11): (video1)
6. Propositional Inference, continued (Sep 14): (video2) (video3)
7. Propositional Inference, continued (Sep 16): (video4) (video5)
8. Resolution (Sep 18): (video1)
9. Resolution, continued (Sep 21): (video2) (video3)
10. Resolution, continued (Sep 23): (video4) (video5)
11. Conversion to CNF (Sep 25): (video)
12. DPLL (Sep 25): (video1)
13. DPLL, continued (Sep 28): (video2) (video3)
14. DPLL, continued (Sep 30): (video4) (video5)
15. Search Spaces (Oct 2): (video1) (video2) (video3)
16. Uninformed Search (Oct 7): (video1) (video2)
17. Uninformed Search, continued (Oct 9): (video3) (video4) (video5)
18. Uninformed Search, continued (Oct 12): (video6) (video7) (video8)
19. Heuristic Search (Oct 14): (video1) (video2)
20. Heuristic Search, continued (Oct 16): (video3)
21. Heuristics (Oct 16): (video1)
22. Analysis of Search (Oct 21): (video1) (video2) (video3)
23. Analysis of Search (Oct 23): (video4)
24. Minimax (Oct 26): (video1) (video2)
25. Minimax, continued (Oct 28): (video3) (video4)
26. Expectimax (Oct 28): (video1)
27. Alpha Beta Pruning (Oct 30): (video1) (video2)
28. Markov Decision Processes (Nov 6): (video1)
29. Markov Decision Processes, continued (Nov 9): (video2) (video3) (video4)
30. Markov Decision Processes, continued (Nov 11): (video5) (video6) (video7)
31. Markov Decision Processes, continued (Nov 13): (video8)
32. Passive Reinforcement Learning (Nov 16): (video1) (video2)
33. Active Reinforcement Learning (Nov 18): (video1) (video2)
34. Active Reinforcement Learning, continued (Nov 20): (video3)
35. Bayesian Networks (Nov 30): (video1) (video2) (video3)
36. Bayesian Networks, continued (Dec 2): (video4) (video5) (video6)
37. Bayesian Networks, continued (Dec 4): (video7) (video8)
38. Bayesian Networks, continued (Dec 7): (video9) (video10) (video11)

PROJECT LINKS

Project 0 (Sudoku):

• Part 1 (due Wednesday, Sep. 9, start of class, through Gradescope):  (pdf)  (solution)
• Part 2 (due Monday, Sep. 14 at noon, through Gradescope): (pdf)  (solution)
• Part 3 (due Wednesday, Sep. 16 at noon, through Github Classroom): (github classroom) (solution)
• Part 4 (due Friday, Sep. 18 at noon, through Github Classroom): (github classroom) (solution)
• Part 5 (due Monday, Sep. 21 at noon, through Github Classroom): (github classroom) (solution)
• Part 6 (due Friday, Sep. 25 at noon, through Github Classroom): (github classroom) (solution)
• Part 7 (due Monday, Sep. 28 at noon, through Github Classroom): (github classroom)
• Part 8 (due Wednesday, Sep. 30 at noon, through Github Classroom): (github classroom) (solution)
• Part 9 (due Friday, Oct. 2 at noon, through Github Classroom): (github classroom)
• Part 10 (due Wednesday, Oct. 7 at noon, through Github Classroom): (github classroom) (solution)

Project 1: Search

• Due Monday, October 19 at noon, through Github Classroom: https://classroom.github.com/a/duWdO4Tp

Project 2: Multiagent Search

• Due Wednesday, November 4 at noon, through Github Classroom: https://classroom.github.com/a/nfmA740f

Project 3: Reinforcement Learning

• Due Monday, November 30 at noon, through Github Classroom: https://classroom.github.com/a/CEbUQlcj

SCHEDULE

Aug 31: Introduction

• Reading (optional): AIMA (Artificial Intelligence: A Modern Approach) Ch. 1

Sep 2: Truth Tables

• Reading: AIMA (Artificial Intelligence: A Modern Approach) 7.1 and 7.2
• Reading (optional): AIMA Chapters 1 and 2.
• Homework (due Saturday, Sep. 5 anytime, through Gradescope): (pdf)

Sep 4: Language and Meaning

• Reading: None
• Homework (due Wednesday, Sep. 9, start of class, through Gradescope): (pdf) (solution)

Sep 9: Propositional Logic

• Reading: AIMA 7.3 and 7.4
• Homework (due Friday, Sep. 11 at noon, through Gradescope): (pdf) (solution)

Sep 11: Propositional Inference

• Reading: None (but catch up on the reading if you haven’t yet read up to 7.4)
• Homework (due Monday, Sep. 14 at noon, through Gradescope): (pdf) (solution)

Sep 14: Propositional Inference

• Reading: AIMA 7.5
• Homework (due Wednesday, Sep. 16 at noon, through Github Classroom): https://classroom.github.com/a/mwsc-ovW
• If you haven’t used Github Classroom before, the following guide may help: https://github.com/Mark-Hopkins-at-Reed/csci-378/blob/master/admin/github.md

Sep 16: Propositional Inference

• Reading: No additional reading. Review previous AIMA and/or lecture notes if desired.
• Homework (due Friday, Sep. 18 at noon, through Github Classroom): https://classroom.github.com/a/CNutnM6X

Sep 18: Resolution

• Reading: Review AIMA 7.5.
• Homework (due Monday, Sep. 21 at noon, through Github Classroom): https://classroom.github.com/a/LTjC4hf3

Sep 21: Soundness of Resolution

• Reading: None
• Homework: None, though Sudoku 6 is due Friday.

Sep 23: Completeness of Resolution

• Reading: None
• Homework (due Friday, Sep. 25 at noon, through Github Classroom): https://classroom.github.com/a/TxMxLnYs

Sep 25: DPLL

• Reading: AIMA 7.6
• Homework (due Monday, Sep. 28 at noon, through Github Classroom): https://classroom.github.com/a/OkvIlc9w

Sep 28: DPLL

• Reading: Review AIMA 7.6, if you haven’t already.
• Homework (due Wednesday, Sep. 30 at noon, through Github Classroom): https://classroom.github.com/a/NU3yd8U8

Sep 30: DPLL

• Reading: Review lecture notes on DPLL.
• Homework (due Friday, Oct 2 at noon, through Github Classroom): https://classroom.github.com/a/gHepY4ve

Oct 2: Search Spaces

• Reading: AIMA 3.1
• Homework: None

Oct 5: Exam 1 (Logic)

Oct 7: Uninformed Search

• Reading: None
• Homework: None

Oct 9: Uninformed Search

• Reading: AIMA 3.2-3.4, and review lecture notes on Search Spaces.
• Homework: None

Oct 12: Uninformed Search

• Reading: None
• Homework (due Wednesday, Oct. 14 at noon, through Gradescope): (pdf) (solution)

Oct 14: Heuristic Search

• Reading: AIMA 3.5, and review lecture notes on Uninformed Search.
• Homework (due Friday, Oct. 16 at noon, through Gradescope): (pdf) (solution)

Oct 16: Heuristics

• Reading: AIMA 3.6.
• Homework: Pacman Project 1 is due on Monday at noon.

Oct 19: Pacman 1 Review

Oct 21: Analysis of Search

• Reading: No additional reading, but review AIMA 3.4-3.6.
• Homework (due Friday, Oct. 23 at noon, through Gradescope): (pdf) (solution)

Oct 23: Iterative Deepening/Multiplayer Games

• Reading: AIMA 5.1
• Homework: None

Oct 26: Minimax

• Reading: AIMA 5.2
• Homework (due Wednesday, Oct 28 at noon, through Gradescope): (pdf) (solution)

Oct 28: Minimax/Expectimax

• Reading: AIMA 5.4, 5.5
• Homework (due Wednesday, Oct 28 at noon, through Gradescope): (pdf) (solution)

Oct 30: Alpha Beta Pruning

• Reading: AIMA 5.3
• Homework: Study for Monday’s exam!

Nov 2: Exam 2 (Search)

Nov 4: Pacman 2 Review

Nov 6: Markov Decision Processes

• Reading: None
• Homework (due Monday, Nov 9 at noon, through Gradescope): Available via Gradescope. The lecture notes on Markov Decision Processes (slides 1 through 4) should be sufficient reading material for this homework. (solution)

Nov 9: Markov Decision Processes

• Reading: None
• Homework: None

Nov 11: Value Iteration

• Reading: AIMA 17.1.1, 17.1.2, 17.2.1
• Homework (due Friday, Nov 13 at noon, through Gradescope): Available via Gradescope. 

Nov 13: Convergence of Value Iteration

• Reading: None
• Homework: None

Nov 16: Passive Reinforcement Learning

• Reading: AIMA 22.1, 22.2
• Homework: None

Nov 18: Active Reinforcement Learning

• Reading: AIMA 22.3, 22.4
• Homework: None

Nov 20: Active Reinforcement Learning

• Reading: None
• Homework: Project 3 is due on the Monday after the break!

Nov 30: Automating Probabilistic Reasoning

• Reading: AIMA 12.1-12.5
• Homework (due Wednesday, Dec 2 at noon, through Gradescope): Available via Gradescope.

Dec 2: Bayesian Networks

• Reading: AIMA 13.1-13.2
• Homework (due Friday, Dec 4 at noon, through Gradescope): Available via Gradescope.

Dec 4: Bayesian Network Inference

• Reading: AIMA 13.3
• Homework (due Monday, Dec 7 at noon, through Gradescope): Available via Gradescope.

Dec 7: Algorithmic Analysis of Variable Elimination

• Reading: Review the posted lecture notes on Variable Elimination.
• Homework: Study for Thursday’s exam!