CSCE 625 - Artificial Intelligence

Fall 2012


Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 845-0161
email: ioerger@cs.tamu.edu
office hours: Tues, 1:00-2:00, or by appointment

TA: Jaewook Yoo
email: jwookyoo@neo.tamu.edu
office hours: Mon, Wed, 12:30-1:30pm, HRBB 339

Meeting: MWF, 10:20 am-11:10, HRBB 113

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs625-fall12/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

  1. To learn about intelligent search methods and their role in building complex problem-solving programs.
  2. To learn about knowledge representation techniques and methods for knowledge-based/intelligent decision-making in programs.
  3. To gain exposure to traditional sub-fields of AI (automated deduction, planning, machine learning, natural language...).
Topics Assignments, Projects, Exams, and Grading

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: 50% homework/programming projects, 50% 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 (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.


Schedule:

Mon, Aug 27first day of class; core concepts
Wed, Aug 29perspectives on AI read Ch. 1
Fri, Aug 31(discussion continued)
Mon, Sep 3Search Algorithmsread Ch. 3 (skip 3.5.3)
Wed, Sep 5iterative deepening
Fri, Sep 7heuristics
Mon, Sep 10A* search
Wed, Sep 12optimality and efficiency of A*
Fri, Sep 14hill climbing, simulated annealingread Ch. 4.1
Mon, Sep 17TSP, genetic algorithmsProject #1 due
Wed, Sep 19Game-playing, minimax searchCh. 5 (skip 5.6)
Fri, Sep 21alpha-beta pruning
Mon, Sep 24Constraint SatisfactionCh. 6 (skip 6.3.3 and 6.5)
Wed, Sep 26heuristics, AC-3
Fri, Sep 28local search for CSPProject #2 due
Mon, Oct 1Mid-term Exam #1
Wed, Oct 3Propositional LogicCh. 7 (skip 7.7 for now)
Fri, Oct 5natural deduction proofs
Mon, Oct 8forward- and backward-chaining
Wed, Oct 10resolution
Fri, Oct 12DPLL, WalkSAT
Mon, Oct 15First-Order Logic, syntaxCh. 8; Homework #3 due, solutions
Wed, Oct 17semantics (model theory)
Fri, Oct 19ontologies, axiomatizing numbers, quantities
Mon, Oct 22temporal reasoning, Events and Interval Logicread Sec 12.1-12.3
Wed, Oct 24inference in FOL; unificationread Ch. 9; Homework #4 due, solutions
Fri, Oct 26natural deduction, resolution in FOL
Mon, Oct 29Herbrand's Theorem
Wed, Oct 31Rete, Expert Systems, Logic Programming (PROLOG)
Fri, Nov 2Description LogicsSec. 12.5.2; Homework #5 due, solutions
Mon, Nov 5Mid-term Exam #2
Wed, Nov 7Default Reasoning (semantic nets, non-monotonic logics, negation in Prolog)Sec. 12.5-12.6
Fri, Nov 9probability, Bayes ruleCh. 13
Mon, Nov 12
Wed, Nov 14Bayesian networksCh. 14.1, 14.2, 14.4
Fri, Nov 16Reasoning about action, situation calculusSec. 7.7, 10.4.2
Mon, Nov 19Frame Problem, PDDLCh. 10; Homework #6 due
Wed, Nov 21goal regressionsee also sec 3.2 of Weld, 1994
Fri, Nov 23Thanksgiving break (no class)
Mon, Nov 26partial-order (nonlinear) planning, GraphPlan, SatPlan due
Wed, Nov 28other planning algorithmsCh. 11; Homework #7 due
Fri, Nov 30Intelligent Agents Ch. 2
Mon, Dec 3last day of class Homework #8 due, solutions
Tues, Dec 11Final Exam, 8:00-10:00


Academic Integrity Statement and Policy

Aggie Code of Honor: An Aggie does not lie, cheat or steal, or tolerate those who do.
see: Honor Council Rules and Procedures


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