CSCE 420 - Artificial Intelligence

Fall 2024

Professor: Dr. Thomas R. Ioerger
email:ioerger@cs.tamu.edu
office:438 Peterson
office hours: (posted on Canvas)

TA: (see Canvas)
email address:
office hours:
location:

Meeting: TR, 8:00-9:15, 124 HRBB

Textbook: Artificial Intelligence: A Modern Approach, 4th US ed. (2020) Stuart Russell and Peter Norvig.

Course Web Page: http://faculty.cs.tamu.edu/ioerger/cs420-fall24/index.html (this page)

Syllabus (contains information about projects, exams, grading policy, etc)

Programming Assignments

The programming assignments will be done (individually) in Python and C++. Programs will have to compile and run on compute.cs.tamu.edu, which is the reference platform. The projects for the course will be submitted via your TAMU accounts on github.com. Students will have to create a (private) repository for this class, and then share that with the instructor and TA by making them collaborators. The date and time students turn in each project will be determined by the timestamp of their commits on their files. It is the student's responsibility to learn how to use Git well enough to commit their code (and reports and other materials required to turn in) and push it to the Github server. Forgetting or being unable to commit and push their files will not be accepted as an excuse for lateness.

Homeworks must be typed (not hand-written), and will be also be turned in via github.

Grading and Late Policy

The weights for the grades will be as follows:
Homeworks: 10% (5 homeworks, 2% each)
Programming Assignments: 35% (3 projects: 10%, 10%, 15%)
Exams: 55% (2 exams: Exam 1: 25%, Exam 2: 30%)
Note: Exam 2 (during finals week) will be non-comprehensive.

The policy for late turn-ins is as follows:

Schedule

topicconceptsreadingassignments
Tues, Aug 20What is AI?perspectives on AI Ch. 1; slides
Thurs, Aug 22core concepts in Symbolic AI
Tues Aug 27 Uninformed Search BFS, DFS, iterative deepening Ch. 3; slides
Thur Aug 29complexity analysis, GraphSearch, Uniform CostHW1 due
Tues Sep 3 Informed/heuristic Search heuristics, Greedy (best-first) search, A*
Thur Sep 5optimality of A*
Tues Sep 10 Iterative Improvement hill-climbing, beam searchCh. 4.1; slides
Thur Sep 12simulated annealing, genetic algorithmsPA1 due; probs.zip
Tues Sep 17 Game Search minimax, alpha-beta pruningCh. 5; slides
Thur Sep 19board eval functions; Deep Blue; AlphaGO (MCTS)
Tues Sep 24 Constraint Satisfactionback-tracking search, CSP heuristicsCh. 6; slides
Thur Sep 26constraint propagation; AC-3 algorithmPA2 due
Tues Oct 1Min-Conflicts algorithmHW2 due
Thurs Oct 3 *** Exam 1 ***covers Ch. 3 (skip 3.5.4-3.5.6), 4.1, 5, 6
Tues Oct 8(Fall Break)
Thurs Oct 10Propositional Logic syntax, semantics/models, entailment, ROICh. 7; slides
Tues Oct 15Inference Algorithms: natural deduction,
forward-chaining, backward-chaining
Thurs Oct 17 resolution refutation, conversion to CNF
Tues Oct 22 Satisfiability; DPLL; hard Sat problems; WalkSATHW3 due
Thurs Oct 24 First-Order Logic syntax, semantics (models), ontologiesCh. 8; slides
Tues Oct 29 Rules of Inference, unification, Natural Deduction proofsCh. 9
Thurs Oct 31(Halloween) Resolution in FOL, conversion to CNF, Herbrand's Theorem
Tues Nov 5 Forward-chaining; Backward-chaining; Expert Systems
Thurs Nov 7 Prologslides, tutorial
Tues Nov 12Uncertainty ReasoningProbabilistic Knowledge Representation, Bayes' Ruleslides;
Ch. 12, 13.1, and first
subsection of 13.2 (p. 415)		PA3 due; convCNF.py
Thurs Nov 14Planning Situation Calculus, Frame Problem, PDDL, forward state-space searchCh. 11 (skip 11.4-5); Sec. 7.7; slides
Tues Nov 19goal regression; other types of plannersHW4 due
Thur Nov 21 Intelligent Agentsagent characteristics, environmentsCh. 2; slides
Tues Nov 26 (last day of class)agent architecturesHW5 due
Thurs Nov 28 (Thanksgiving)
Fri, Dec 6, 1:00-3:00pm*** Exam II *** (non-comprehensive)Ch. 7, 8, 9, Ch. 11 (skip 11.4-5), Ch. 12, 13.1, first subsection of 13.2; Ch. 2