CSCE 420 - Artificial Intelligence

Spring 2013


Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 458-5518
email: ioerger@cs.tamu.edu
office hours: Wednesdays, 3-4pm

TA: Wen Li
email: wen.li@neo.tamu.edu
office hours: Tu/Th 5:00-6:00, 311D Bright

Meeting: TR, 9:35-10:50 am, HRBB 113

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs420-spr13/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) - Note, the real intention is that students have a solid understanding of data structures and algorithms before taking this class, as would be learned in CSCE 221.

Textbook

Russell, S. and Norvig, P. (2009). 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.
    1. to learn how to formulate computational problems as search
    2. to learn how various search algorithms work
    3. to learn their computational properties (space- and time-complexity)
    4. to learn how heuristics can improve efficiency of search
  2. To learn about knowledge representation techniques and methods for knowledge-based/intelligent decision-making in programs.
    1. to learn syntax and semantics of propositional logic and first-order logic
    2. to learn how inference algorithms work
    3. to learn the advantages of alternative knowledge respresentation systems
    4. to learn how to represent and reason about uncertainty using Bayesian probability
  3. To gain exposure to traditional sub-fields of AI (automated deduction, planning, machine learning, natural language...).
    1. to learn how symbolic planning algorithms work
    2. to learn different decision-making architectures for intelligent agents
    3. to learn how machine learning can be used to generalize from experience/examples
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 be a mid-term exam 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:

Tues, Jan 15first day of class; core concepts in AI
Thurs, Jan 17perspectives on AIread Ch. 1
Tues, Jan 22Search Algorithms; DFS, BFSread Ch. 3 (skip 3.5.3)
Thurs, Jan 24uniform cost, iterative deepening
Tues, Jan 29heuristics, A*
Thurs, Jan 31
Tues, Feb 5hill-climbing, simulated annealingread Sec 4.1 (skip genetic algorithms);
Homework #1 due (solutions)
Thurs, Feb 7Constraint SatisfactionCh. 6
Tues, Feb 12AC-3, MACbacktracking alg, AC-3, and MAC
Thurs, Feb 14Game SearchCh. 5
Tues, Feb 19Homework #2 due (solutions)
Thurs, Feb 21Propositional Logic (syntax, semantics, entailment, model checking)Ch. 7.1-7.4
Tues, Feb 26proof procedures: natural deduction, forward-chaining, backward-chainingSec. 7.5; notes on backchaining
Thurs, Feb 28resolution, satisfiability, DPLLSec. 7.5.2
Tues, Mar 5Mid-term Exam (covers Ch. 3-7, except: WalkSat, 7.7)
Thurs, Mar 7WalkSatSec. 7.6.2-7.6.2; slides on WalkSat
Tues, Mar 12(Spring Break)
Thurs, Mar 14(Spring Break)
Tues, Mar 19First-order logic: syntax and semanticsCh. 8; examples of models in FOL
Thurs, Mar 21unification and inference in FOLCh. 9; Homework #3 due, solutions
unification algorithm, examples of inference in FOL
Tues, Mar 26forward/backward chaining in FOL; resolutionSec 9.3-9.5
Thurs, Mar 28PrologSec 9.4; my notes (mini-tutorial) on Prolog
Tues, Apr 2ontologies, Event Calculus;
other KR systems: semantics nets, description logics, OWL
Sec 12.1-12.3, 12.5
Thurs, Apr 4exceptions and uncertainty - default logic, negation-as-failure, inheritanceSec. 12.6
Tues, Apr 9probability, Bayes RuleCh. 13, HW #4 due, solutions
Thurs, Apr 11Bayesian NetworksSec. 14.1, notes
Tues, Apr 16Intelligent Agents, slides Ch. 2, HW #5 due solutions
Thurs, Apr 18Planning: Situation Calculus and the Frame ProblemSec 10.4.2, 7.7.1
Tues, Apr 23PDDL (STRIPS operators), goal-regression algorithmSec. 10.1-10.2; see also Sec 3.2 of (Weld, 1994)
Thurs, Apr 25(last day of class) non-linear planning, POP10.4.4; see also Sec 4.2 of (Weld, 1994)
Wed, May 1 final office hours (322C HRBB, 3:00-4:00) HW6 due
Fri, May 3Final Exam, 12:30-2:30


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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