CSCE 625 - Artificial Intelligence

Fall 2017

Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 458-5518
email:ioerger@cs.tamu.edu
office hours: Wed, 1:00-2:00

TA: Patrick Chinprutthiwong
email:cpx0rpc@tamu.edu
office:501B HRBB
office hours:Tues, 3:00-4:00

Meeting: TR, 12:45-2:00, CHEN 104

Course Web Page/Syllabus (this page): https://people.engr.tamu.edu/ioerger/cs625-fall17/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 4-6 homeworks or programming assignments.
There will be one mid-term exam, and a non-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, Aug 29(class cancelled due to Hurricane Harvey)
Thurs, Aug 31first day of classWhat is AI?perspectives; core conceptsCh. 1
Tues, Sept 5Search AlgorithmsBFS, DFS, complexity analysisCh. 3 (skip 3.5.3); slides
Thurs, Sept 7iterative deepening, uniform cost search
Tues, Sept 12(guest lecture, Dr. Dylan Shell)Iterative Improvement search hill-climbing, beam searchCh. 4.1 (skip 4.1.4)
Thurs, Sept 14heuristics, greedy search, A* Sec 3.5.2, 3.6
Tues, Sept 19(guest lecture, Dr. Michael DeJesus)simulated annealingSec 4.1.2; slides
Thurs, Sept 21 Game Search Algorithmsminimax, alpha-beta pruning Ch. 5; slides; AlphaGo
Tues, Sept 26Constraint Satisfactionbacktracking searchCh. 6 (skip 6.5), slides
Thurs, Sept 28MRV heuristic; constraint propagation application of CSP to computer vision
Tues, Oct 3AC-3; MinConflicts
Thurs, Oct 5Project #1 duePropositional Logicsyntax, semanticsCh. 7 (we will cover Sec 7.7 on SatPlan later in the semester)
Tues, Oct 10inference algorithms: natural deduction slides
Thurs, Oct 12mid-term exam
Tues, Oct 17resolution
Thurs, Oct 19satisfiability and DPLL
Tues, Oct 24First Order Logicsyntax, ontologiesCh. 8, Ch 12.1-2
Thurs, Oct 26model theory
Tues, Oct 31Homework #1 dueinference in FOL; unificationCh. 9; slides
Thurs, Nov 2resolution in FOL
Tues, Nov 7forward-chaining (Rete, Jess);
back-chaining (Prolog)
Thurs, Nov 9 temporal reasoning, Event Calculus, Interval Logic 12.3
Tues, Nov 14limitations of FOL; negation as failure; default logic, circumscription; description logics?Sec 12.5-6; slides
Thurs, Nov 16uncertainty, probabilityCh. 13, Sec 14.1, notes
Tues, Nov 21Homework #2 dueIntelligent Agentsagent environments and architecturesCh. 2, slides
Thurs, Nov 23class cancelled (Thanksgiving)
Tues, Nov 28Planningsituation calculus, Frame ProblemSec 7.7, Sec 10.4.2
Thurs, Nov 30STRIPS, goal-regression Ch. 10 (skip 10.3); GoalRegr alg
Tues, Dec 5last day of classPOP, SatPlan
Thurs, Dec 7Project #2 due by 1:00pm via Turnin
Wed, Dec 13final exam: 8:00-10:00am

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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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