CSCE 625 - Artificial Intelligence

Fall 2015

Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 458-5518
email:ioerger@cs.tamu.edu
office hours: Thurs, 11:00-12:00

TA: Michael A. DeJesus
email:mad@cs.tamu.edu
office: 322D Bright
office hours:Tues, 11:00-12:00

Meeting: TR, 9:35-10:50am, 108 CHEN

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs625-fall15/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 (or an equivalent undergraduate course on data structures and algorithms)

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 approximately 8 programming assignments or homeworks.
There will be only be one exam, a comprehensive final exam at the end of the semester during finals week.

The final grade for the course will be determined from the weighted-average above as follows:

The penalty for late assignments is -5% per day (pro-rated over 24 hours).
After 10 days late, the deductions cease, and 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:

assignmenttopicconceptsreading
Tues, Sept 1first day of classWhat is AI?perspectives; core conceptsCh. 1
Thurs, Sept 3Search AlgorithmsBFS, DFSCh. 3 (skip 3.5.3)
Tues, Sept 8ID, UC, Greedy
Thurs, Sept 10heuristics, A*
Tues, Sept 15Iterartive Improvement search hill-climbing
beam search		Ch. 4.1 (skip 4.1.4)
Thurs, Sept 17simulated annealing
Tues, Sept 22 Proj 1 due; ATM.graph;
path visualization tool 		Game Search Algorithmsminimax, alpha-beta pruning Ch. 5
application of search to robot motion planning
Thur, Sept 24real-world game playing
Tues, Sept 29(guest lecture by Dr. Dylan Shell)
Thurs, Oct 1Constraint Satisfactionbacktracking searchCh. 6 (skip 6.5)
Tues, Oct 6Proj 2 dueMRV heuristic
Thurs, Oct 8AC-3vision as CSP
Tues, Oct 13Propositional Logicsyntax, semanticsCh. 7, Sec 12.1-2
Thurs, Oct 15inference algorithms: natural deduction
Tues, Oct 20Proj 3 dueresolution
Thurs, Oct 22(discussion of IBM's Watson)
Tues, Oct 27satisfiability and DPLL
Thurs, Oct 29First Order LogicsyntaxCh. 8
Tues, Nov 3Proj 4 duemodel theory
Thurs, Nov 5inference in FOLCh. 9
Tues, Nov 10unification; forward-chaining (Rete, Jess)
Thurs, Nov 12back-chaining, Prolog
Tues, Nov 17Proj 5 duetemporal reasoning, Event Calculus, Interval Logic 12.3
Thurs, Nov 19uncertainty, default reasoning, probabilitySec 12.5-6, Ch. 13, Sec 14.1, notes
Tues, Nov 24Homework 6 due PDFIntelligent Agentsagent environments and architecturesCh. 2
Thurs, Nov 26class cancelled (Thanksgiving)
Tues, Dec 1Planningsituation calculus, Frame ProblemCh. 10 (skip 10.3), Sec 7.7.1
Thurs, Dec 3STRIPS, goal-regression
Tues, Dec 8last day of class
Homework 7 due		POP, SatPlanGoalRegr alg
Fri, Dec 11final exam, 12:30-2:30

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see: Honor Council Rules and Procedures

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