Professor: Dr. Thomas R. Ioerger
Office: | 322C Bright Bldg. |
Phone: | 458-5518 |
email: | ioerger@cs.tamu.edu |
office hours: | Wed, 1:00-2:00 |
email: | cpx0rpc@tamu.edu |
office: | 501B HRBB |
office hours: | Tues, 3:00-4:00 |
Meeting: TR, 12:45-2:00, CHEN 104
Course Web Page/Syllabus (this page): https://people.engr.tamu.edu/ioerger/cs625-fall17/index.html
Course Description (from TAMU course catalog): Basic concepts and methods of artificial intelligence; Heuristic search procedures for general graphs; game playing strategies; resolution and rule based deduction systems; knowledge representation; reasoning with uncertainty.
Prerequisites: CSCE 221 (or an equivalent undergraduate course on data structures and algorithms)
Course Objectives
The work for this course will consist of approximately 4-6 homeworks or
programming assignments.
There will be one mid-term exam, and
a non-comprehensive final exam
at the end of the semester during finals week.
The penalty for late assignments is -5% per day (pro-rated
over 24 hours).
After 10 days late, the deductions cease, and the maximum
loss of points is 50%. As long as you
turn an assignment in by the end of the semester, it could still be
worth as much as half-credit. This is to encourage you to eventually complete
the assignment, even if you can't get it in on time initially.
assignment | topic | concepts | reading | |
---|---|---|---|---|
Tues, Aug 29 | (class cancelled due to Hurricane Harvey) | |||
Thurs, Aug 31 | first day of class | What is AI? | perspectives; core concepts | Ch. 1 |
Tues, Sept 5 | Search Algorithms | BFS, DFS, complexity analysis | Ch. 3 (skip 3.5.3); slides | |
Thurs, Sept 7 | iterative deepening, uniform cost search | |||
Tues, Sept 12 | (guest lecture, Dr. Dylan Shell) | Iterative Improvement search | hill-climbing, beam search | Ch. 4.1 (skip 4.1.4) |
Thurs, Sept 14 | heuristics, greedy search, A* | Sec 3.5.2, 3.6 | ||
Tues, Sept 19 | (guest lecture, Dr. Michael DeJesus) | simulated annealing | Sec 4.1.2; slides | |
Thurs, Sept 21 | Game Search Algorithms | minimax, alpha-beta pruning | Ch. 5; slides; AlphaGo | |
Tues, Sept 26 | Constraint Satisfaction | backtracking search | Ch. 6 (skip 6.5), slides | |
Thurs, Sept 28 | MRV heuristic; constraint propagation | application of CSP to computer vision | ||
Tues, Oct 3 | AC-3; MinConflicts | |||
Thurs, Oct 5 | Project #1 due | Propositional Logic | syntax, semantics | Ch. 7 (we will cover Sec 7.7 on SatPlan later in the semester) |
Tues, Oct 10 | inference algorithms: natural deduction | slides | ||
Thurs, Oct 12 | mid-term exam | |||
Tues, Oct 17 | resolution | |||
Thurs, Oct 19 | satisfiability and DPLL | |||
Tues, Oct 24 | First Order Logic | syntax, ontologies | Ch. 8, Ch 12.1-2 | |
Thurs, Oct 26 | model theory | |||
Tues, Oct 31 | Homework #1 due | inference in FOL; unification | Ch. 9; slides | |
Thurs, Nov 2 | resolution in FOL | |||
Tues, Nov 7 | forward-chaining (Rete, Jess); back-chaining (Prolog) | |||
Thurs, Nov 9 | temporal reasoning, Event Calculus, Interval Logic | 12.3 | ||
Tues, Nov 14 | limitations of FOL; negation as failure; default logic, circumscription; description logics? | Sec 12.5-6; slides | ||
Thurs, Nov 16 | uncertainty, probability | Ch. 13, Sec 14.1, notes | ||
Tues, Nov 21 | Homework #2 due | Intelligent Agents | agent environments and architectures | Ch. 2, slides |
Thurs, Nov 23 | class cancelled (Thanksgiving) | |||
Tues, Nov 28 | Planning | situation calculus, Frame Problem | Sec 7.7, Sec 10.4.2 | |
Thurs, Nov 30 | STRIPS, goal-regression | Ch. 10 (skip 10.3); GoalRegr alg | ||
Tues, Dec 5 | last day of class | POP, SatPlan | ||
Thurs, Dec 7 | Project #2 due by 1:00pm via Turnin | |||
Wed, Dec 13 | final exam: 8:00-10:00am | |||