CSCE 420 - Artificial Intelligence

Fall 2023

Professor: Dr. Thomas R. Ioerger
email:ioerger@cs.tamu.edu
office:438 Peterson
office hours: Tues and Thurs, 12:30-2:00 pm

TA: Daniel Ortiz-Chaves
email address: dortizchaves@email.tamu.edu
office hours: Tues and Thurs, 3:45-4:45 pm
location: 113 EABB

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-fall23/index.html (this page)

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

Campuswire discussion board

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:
Homeworks: 15% (5 homeworks, 3% each)
Programming Assignments: 30% (2 programming projects, 15% each)
Exams: 55% (exam 1: 25%, exam 2: 30%)

The policy for late turn-ins is as follows:

Schedule

topicconceptsreadingassignments
Tues, Aug 22What is AI?perspectives on AI; core concepts Ch. 1; slides
Thurs, Aug 24
Tues Aug 29 Uninformed Search BFS, DFS, iterative deepening Ch. 3; slides
Thur Aug 31complexity analysis, GraphSearch, Uniform Cost
Tues Sep 5 Informed/heuristic Search heuristics, Greedy (best-first) search, A*
Thur Sep 7optimality of A*
Tues Sep 12 Iterative Improvement hill-climbing, beam searchCh. 4.1; slides
Thur Sep 14simulated annealing, genetic algorithms
Tues Sep 19 Game Search minimax, alpha-beta pruningCh. 5; slides
Thur Sep 21board eval functions; Deep Blue; AlphaGO (MCTS)
Tues Sep 26 Constraint Satisfactionback-tracking search, CSP heuristicsCh. 6; slides
Thur Sep 28AC-3; (vision); min-conflicts algPA1 due; probs.zip
Tues Oct 3Propositional Logic syntax, semantics/models, entailment, ROICh. 7; slides
Thurs Oct 5Inference Algorithms: natural deduction,
forward-chaining, backward-chaining		HW1 due
Tues Oct 10(Fall Break)
Thur Oct 12 *** Exam 1 ***covers Ch. 3 (skip 3.5.4-3.5.6), 4.1, 5, 6
Tues Oct 17 resolution refutation, conversion to CNF
Thurs Oct 19 Satisfiability; DPLL; hard Sat problems; WalkSAT
Tues Oct 24 First-Order Logic syntax, semantics (models), ontologiesCh. 8; slidesHW2 due
Thurs Oct 26 Rules of Inference, unification, Natural Deduction proofsCh. 9
Tues Oct 31 Resolution in FOL, conversion to CNF, Herbrand's Theorem
Thurs Nov 2 Forward-chaining; Backward-chaining; Expert Systems
Tues Nov 7 Prologslides, tutorial
Thurs Nov 9 Uncertainty Reasoning default reasoning, nonmonotonic logics, negation in PrologCh. 10.6; slidesPA2 due; convCNF.py
Tues Nov 14Probabilistic Knowledge Representation, Bayes' RuleCh. 12, 13.1, and first
subsection of 13.2 (p. 415)
Thurs Nov 16Planning Situation Calculus, Frame Problem, PDDL, Forward SSSCh. 11 (skip 11.4-5); Sec. 7.7; slidesHW3 due
Tues Nov 21goal regression; other types of planners
Thur Nov 23(Thanksgiving)
Tues Nov 28 Intelligent Agentsagent characteristics, environmentsCh. 2; slidesHW4 due
Thur Nov 30 (last day of class)agent architectures
Tues Dec 5 (office hours only)(no lecture)HW5 due
Tues, Dec 12, 1:00-3:00pm*** Exam II *** (non-comprehensive)Ch. 7, 8, 9, 10.6, Ch. 11 (skip 11.4-5), Ch. 12, 13.1, first subsection of 13.2; Ch. 2