Professor: Dr. Thomas R. Ioerger
Office: | 322C Bright Bldg. |
Phone: | 458-5518 |
email: | ioerger@cs.tamu.edu |
office hours: | Mon, 3:00-4:00 |
TA: Eric Nelson
email: | ejn8411@tamu.edu |
office: | RDMC-B021 |
office hours: | Wednesday 2pm - 4pm |
Meeting: TR, 2:20-3:35, HRBB 113
Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs420-fall14/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 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.
assigment | topic | concepts | reading | |
---|---|---|---|---|
Tues, Sept 2 | (first day of class) | What is AI? | perspectives on AI | read Ch. 1 |
Thurs, Sept 4 | Intelligent Agents | decision-making; architectures; core concepts in AI | read Ch. 2 | |
Tues, Sept 9 | Search Algorithms | DFS, BFS, greedy | read Ch. 3 (skip 3.5.3) | |
Thurs, Sept 11 | uniform cost, iterative deepening | |||
Tues, Sept 16 | heuristics, A* | |||
Thurs, Sept 18 | Program 1 due (BFS), ATM.graph data file, robot motion planning | Optimization | hill-climbing, simulated annealing | read Sec 4.1 |
Tues, Sept 23 | Constraint Satisfaction | backtracking, heuristics | read Ch. 6 | |
Thurs, Sept 25 | AC-3, MAC | backtracking alg, AC-3, and MAC | ||
Tues, Sept 30 | Program 2 due (DFS, GREEDY) | guest lecture by: Dr. Yoonsuck Choe, | Intro to Machine Learning | |
Thurs, Oct 2 | Game Search | minimax algorithm | read Ch. 5 | |
Tues, Oct 7 | alpha/beta pruning, board evaluation functions | |||
Thurs, Oct 9 | Program 3 due (A*, Block-stacking) | Propositional Logic | syntax, semantics | read Ch. 7 |
Tues, Oct 14 | inference procedures | natural deduction, forward chaining | ||
Thurs, Oct 16 | backward chaining, resolution |
backchaining algorithm, example of NatDed and Res | ||
Tues, Oct 21 |
Program 4 due texas-cities.dat TourOfTexas mapping tool | satisfiability, DPLL, WalkSAT | slides on DPLL | |
Thurs, Oct 23 | First-Order Logic | quantifiers, model theory | read Ch. 8 | |
Tues, Oct 28 | using FOL (concept representation; translation); ontologies | Sec. 12.1-12.2 | ||
Thurs, Oct 30 | Event Calculus | Sec 12.3 | ||
Sat, Nov 1 | Program 5 due (CSP) | due Sat by midnight | ||
Tues, Nov 4 | unification, inference procedures | read Ch. 9 | ||
Thurs, Nov 6 | (continued) | |||
Tues, Nov 11 | Program 6
due
graph.interconnect3 criss cross example | Prolog | mini-tutorial on Prolog | |
Thurs, Nov 13 | Default Reasoning | non-monotonic logics, Semantic Nets, negation-as-failure in Prolog | Sec 12.6 | |
Tues, Nov 18 | Probability | Bayes Rule, Bayesian networks, MDPs | Ch. 13 | |
Thurs, Nov 20 | Reasoning about action | Situation Calculus; Frame Problem | Sec 10.4.2 | |
Tues, Nov 25 | Planning algorithms | PDDL (STRIPS); goal regression | Sec. 10.1-10.2; see also Sec 3.2 of (Weld, 1994) | |
Thurs, Nov 27 | (class cancelled) | (Thanksgiving) | ||
Tues, Dec 2 | HW1 due | more discussion of planning | ||
Thurs, Dec 4 | other types of planners | Sec 11.1-11.2 | ||
Tues, Dec 9 | HW2 due | (last day of class) | review for final | |
Wed, Dec 17 | final exam, 1:00-3:00 | |||