Professor: Dr. Thomas R. Ioerger
Office: | 322C Bright Bldg. |
email: | ioerger@cs.tamu.edu |
office hours: | Wed, 10:00-11:00 |
TA: Vijaya Singh
email address: | mailvijayasingh@tamu.edu |
office location: | RDMC-B021 |
office hours: | Monday: 1:30PM to 3:30PM, Thursday: 12:45PM to 3:00 PM |
Meeting: TR, 11:10-12:25, HRBB 113
Course Web Page: https://people.engr.tamu.edu/ioerger/cs420-fall16/index.html (this page)
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 315 (Programming Studio)
Textbook
Course Objectives
The work for this course will consist of a mix of homeworks, programming assignments, and exams. The overall score for the course will be a weighted combination of these three components, which is tentatively set as follows:
The penalty for late assignments is -5% per day (pro-rated
over 24 hours).
After 10 days late, the deductions cease; 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 30 | (first day of class) | What is AI? | perspectives on AI | read Ch. 1 |
Thurs, Sept 1 | core concepts in AI | |||
Tues, Sept 6 | Search Algorithms | DFS, BFS, iterative deepening | read Ch. 3 (skip 3.5.3), slides | |
Thurs, Sept 8 | Heuristic Search | uniform cost search, heuristics, greedy best-first search | ||
Tues, Sept 13 | guest lecture: Dr. Yoonsuck Choe | A* algorithm | slides | |
Thurs, Sept 15 | implementation ideas for Project 1 | |||
Tues, Sept 20 | Iterative Improvement | hill-climbing, simulated annealing | Sec 4.1; slides | |
Thurs, Sept 22 | Game Search | minimax, alpha-beta pruning | Ch. 5 | |
Tues, Sept 27 | board eval functions, Deep Blue | |||
Thurs, Sept 29 | Project #1 due; ATM_graph2.txt | Constraint Satisfaction | back-tracking algorithm | Ch. 6 |
Tues, Oct 4 | heuristics to make CSP search more efficient | |||
Thurs, Oct 6 | constraint-propagation, AC-3, MAC, min-conflicts | slides with CSP algs | ||
Tues, Oct 11 | Propositional Logic | syntax, semantics | Ch. 7 | |
Thurs, Oct 13 | Project
#2 due Friday at midnight, State1.txt State2.txt State5.txt State6.txt State7.txt State8.txt | inference methods | ||
Tues, Oct 18 | *** Mid-term exam *** | |||
Thurs, Oct 20 | Natural Deduction; Forward-chaining; Backward-chaining | slides on propositional inference algorithms | ||
Tues, Oct 25 | Resolution | |||
Thurs, Oct 27 | Satisfiability, DPLL, WalkSat | |||
Tues, Nov 1 | First-Order Logic | syntax, examples | Ch. 8 | |
Thurs, Nov 3 | Homework #3 due | model theory, ontologies | ||
Tues, Nov 8 | Inference in FOL | unification | Ch. 9 | |
Thurs, Nov 10 | forward-chaining, backward-chaining, and resolution inference in FOL | Jess, PROLOG (see links below), algs | ||
Tues, Nov 15 | Limitations of FOL; alternative KR systems | defeasible reasoning; negation-as-failure in PROLOG; non-monotonic logics and circumscription; fuzzy logic; semantic nets and inheritance; probability and Bayes Rule | 9.4.2, 12.5.1, 12.6, 13.1-2; slides | |
Thurs, Nov 17 | Project 4 due | PROLOG | my notes on Prolog | |
Tues, Nov 22 | Situation Calculus | |||
Thurs, Nov 24 | Thanksgiving (class cancelled) | |||
Tues, Nov 29 | Planning | goal regression | Ch. 10 (skip 10.3) | |
Thurs, Dec 1 | Intelligent Agents | Ch 2, slides | ||
Tues, Dec 6 | (last day of class); Project #5 due | IBM Watson | ||
Fri, Dec 9 | final exam, 3:00-5:00 (113 HRBB) | |||