CSCE 420 - Artificial Intelligence

Spring 2023

Professor: Dr. Thomas R. Ioerger
email:ioerger@cs.tamu.edu
office:438 Peterson
office hours: Tues 12:00-2:00

TA: Jeff Hykin
email address: jeff.hykin@tamu.edu
office hours: Thurs 3:45 - 4:45, or by appointment
location: EABC cubicals

Meeting: TR, 2:20-3:35, 310 ZACH

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-spr23/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. Written homeworks will be also be turned in via github.

Grading and Late Policy

The weights for the grades will be as follows: The policy for late turn-ins is as follows:

Schedule

topicconceptsreadingassignments
Tues, Jan 17What is AI?perspectives on AI; core concepts Ch. 1; slides
Thurs, Jan 19
Tues Jan 24 Uninformed Search BFS, DFS, iterative deepening Ch. 3; slides
Thur Jan 26complexity analysis, GraphSearch, Uniform Cost
Tues Jan 31 Informed/heuristic Search heuristics, Greedy, A*
Thur Feb 2optimality
Tues Feb 7 Iterative Improvement hill-climbing, simulated annealingCh. 4.1; slides
Thur Feb 9genetic algorithms
Tues Feb 14 Game Search minimax, alpha-beta pruningCh. 5; slides
Thur Feb 16board eval functions; Deep Blue; AlphaGO (MCTS)PA1 due (A* for pacman); handout HW1 and PA2
Tues Feb 21 Constraint Satisfactionback-tracking search, CSP heuristicsCh. 6; slides
Thur Feb 23AC-3; (vision); min-conflictsHW1 due (solutions) (written HW on minimax and CSP)
Tues Feb 28*** Exam 1 ***covers Ch. 3 (skip 3.5.4-3.5.6), 4.1, 5, 6EXAM I
Thur Mar 2Propositional Logic syntax, semantics/models, entailment, ROICh. 7; slides
Tues Mar 7Inference algorithms natural deduction proofs, FC, BC
Thur Mar 9resolution refutation, conversion to CNF
Tues Mar 14 (Spring Break)
Thur Mar 16 (Spring Break)
Tues Mar 21 Satisfiability DPLL; hard Sat problems; WalkSATPA2 due (minimax for tic-tac-toe)
Thur Mar 23 FOL syntax, semantics (models), ontologiesCh. 8; slides
Tues Mar 28 Inference in FOL Rules of Inference, unification, Natural Deduction proofsCh. 9HW2 due (PropLog) solutions
Thur Mar 30 Resolution in FOL, conversion to CNF, Herbrand's Theorem
Tues Apr 4 Forward-chaining; Backward-chaining; Expert Systems HW3 due (FOL) solutions
Thur Apr 6 *** Exam II ***(covers Ch. 7-9)EXAM II
Tues Apr 11 Uncertainty default reasoning, nonmonotonic logics, negation in PrologCh. 10.6; slides
Thur Apr 13Probabilistic Knowledge Representation, Bayes' RuleCh. 12, 13.1, and first
subsection of 13.2 (p. 415)
Tues Apr 18Planning Situation Calculus, Frame Problem, PDDL, Forward SSSCh. 11 (skip 11.4-5); Sec. 7.7; slides
Thur Apr 20goal regression; other types of planners
Tues Apr 25 Intelligent AgentsCh. 2; slidesPA3 due (Sammy/map/queens using DPLL)
Thur Apr 27 (last day of class)
Tues, May 2(no class)office hours 12:00-2:00HW4 due (uncertainty; planning); solutions
Tues, May 9, 1:00-3:00pm*** Exam III ***(non-comprehensive; Ch. 2, 7.7, 10.6, 11 (skip 11.4-5), 12, 13.1, 1st page of 13.2)EXAM III