Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 845-0161
email: ioerger@cs.tamu.edu
office hours: Tues, 3:00pm, or by appointment (email to set up a time)
TA: Peihong Guo
office hours:
Meeting: MWF, 10:20 am-11:10, HRBB 113
Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs625-fall11/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 (Data Structures and Algorithms)
Textbook
Russell, S. and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. 3rd edition (blue cover). Prentice Hall.
Course Objectives
The work for this course will consist of a mix of homework assignments, programming projects, and exams. The final grade for the course will be a weighted combination of these three components, which is tentatively set as follows: 33% homework, 33% programming projects, 33% exams. There will most likely be 2 mid-term exams and a final exam. The minimum score for an grade of an A will be 90%, the minimum for a B will be 80%, 70% for C, and so on, though these thresholds may be lowered depending on the performance of the class overall. The penalty for late assignments is -5% per day (24 hours).
| Mon, Aug 29 | first day of class; core concepts | |||||||||
| Wed, Aug 31 | perspectives on AI | read Ch. 1 | ||||||||
| Fri, Sep 2 | history of AI | |||||||||
| Mon, Sep 5 | intelligent agents (characteristics, environments, architectures) | read Ch. 2 | ||||||||
| Wed, Sep 7 | search algorithms | read Sec 3.1-3.4 | ||||||||
| Fri, Sep 9 | DFS, BFS, iterative deepening | |||||||||
| Mon, Sep 12 | heuristic search, A* | read Sec 3.5-3.6 | ||||||||
| Wed, Sep 14 | adversarial search (minimax) | read Ch. 5; Homework #1 due | ||||||||
| Fri, Sep 16 | alpha-beta pruning | |||||||||
| Mon, Sep 19 | constraint satisfaction | read Ch. 6 | ||||||||
| Wed, Sep 21 | AC-3 algorithm | Homework #2 due | ||||||||
| Fri, Sep 23 | CSP examples; scheduling; satisfiability | |||||||||
| Mon, Sep 26 | (class cancelled) | |||||||||
| Wed, Sep 28 | local search (hill-climbing, simulated annealing) | read Sec 4.1 | ||||||||
| Fri, Sep 30 | review | Homework #3 due | ||||||||
| Mon, Oct 3 | Mid-term Exam #1 (in class) | covers Chapters 1-6 | ||||||||
| Wed, Oct 5 | propositional logic, syntax and semantics | read Ch. 7 | ||||||||
| Fri, Oct 7 | inference methods; DPLL | |||||||||
| Mon, Oct 10 | natural deduction, back-chaining | Notes | ||||||||
| Wed, Oct 12 | resolution | |||||||||
| Fri, Oct 14 | First-order logic | Homework #4 due, read Ch. 8 | ||||||||
| Mon, Oct 17 | ||||||||||
| Wed, Oct 19 | fluents, intervals, and events | |||||||||
| Fri, Oct 21 | Automated deduction in FOL; examples | read Ch. 9 | ||||||||
| Mon, Oct 24 | Unification algorithm, Herbrand's Theorem | |||||||||
| Wed, Oct 26 | real-world theorem provers | Homework #5 due | ||||||||
| Fri, Oct 28 | Prolog; SWI; tutorial | |||||||||
| Mon, Oct 31 | examples | |||||||||
| Wed, Nov 2 | tractable inference: description logics, rete algorithm notes | Ch. 1, The Description Logic Handbook | ||||||||
| Fri, Nov 4 | default reasoning, notes | Homework #6 due, read Sec 12.6 | ||||||||
| Mon, Nov 7 | Mid-term Exam #2 | |||||||||
| Wed, Nov 9 | Planning - Situation Calculus, frame problem | read Ch. 10 | ||||||||
| Fri, Nov 11 | state-space planners, PDDL, STRIPS assumption | |||||||||
| Mon, Nov 14 | goal regression (10.2.2); partial-order planning (10.4.4) | read (Weld, 1994) | ||||||||
| Wed, Nov 16 | GraphPlan | |||||||||
| Fri, Nov 18 | SatPlan, scheduling; notes | read Ch. 11 | ||||||||
| Mon, Nov 21 | HTNs; plan monitoring and repair | |||||||||
| Wed, Nov 23 | Markov Decision Processes; notes | read Ch 17.1-17.2 | ||||||||
| Fri, Nov 25 | (Thanksgiving break) | |||||||||
| Mon, Nov 28 | Probabilistic representations of uncertainty in knowledge | read Ch. 13 | ||||||||
| Wed, Nov 30 | Bayesian networks, notes | read Ch. 14.1-14.2; Homework #7 due| Fri, Dec 2 | inference in Bayesian networks, notes | read Ch 14.4-14.5 | Mon, Dec 5 | last day of class | Homework #8 due | Fri, Dec 9 | Final Exam, 3:00-5:00 | |
