CSCE 420 - Artificial Intelligence

Fall 2016

Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
email:ioerger@cs.tamu.edu
office hours: Wed, 10:00-11:00

TA: Vijaya Singh
email address:mailvijayasingh@tamu.edu
office location: RDMC-B021
office hours: Monday: 1:30PM to 3:30PM, Thursday: 12:45PM to 3:00 PM

Meeting: TR, 11:10-12:25, HRBB 113

Course Web Page: https://people.engr.tamu.edu/ioerger/cs420-fall16/index.html (this page)

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)

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 homeworks, programming assignments, and exams. The overall score for the course will be a weighted combination of these three components, which is tentatively set as follows:

The final grade will be determined from the weighted-average score as follows:

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:

assignmenttopicconceptsreading
Tues, Aug 30(first day of class)What is AI?perspectives on AI read Ch. 1
Thurs, Sept 1core concepts in AI
Tues, Sept 6Search AlgorithmsDFS, BFS, iterative deepeningread Ch. 3 (skip 3.5.3), slides
Thurs, Sept 8Heuristic Searchuniform cost search, heuristics, greedy best-first search
Tues, Sept 13guest lecture: Dr. Yoonsuck ChoeA* algorithmslides
Thurs, Sept 15implementation ideas for Project 1
Tues, Sept 20Iterative Improvementhill-climbing, simulated annealingSec 4.1; slides
Thurs, Sept 22Game Searchminimax, alpha-beta pruningCh. 5
Tues, Sept 27board eval functions, Deep Blue
Thurs, Sept 29Project #1 due; ATM_graph2.txt Constraint Satisfactionback-tracking algorithmCh. 6
Tues, Oct 4heuristics to make CSP search more efficient
Thurs, Oct 6constraint-propagation, AC-3, MAC, min-conflictsslides with CSP algs
Tues, Oct 11Propositional Logicsyntax, semanticsCh. 7
Thurs, Oct 13Project #2 due Friday at midnight,
State1.txt State2.txt
State5.txt State6.txt
State7.txt State8.txt 		inference methods
Tues, Oct 18*** Mid-term exam ***
Thurs, Oct 20Natural Deduction; Forward-chaining; Backward-chainingslides on propositional inference algorithms
Tues, Oct 25Resolution
Thurs, Oct 27Satisfiability, DPLL, WalkSat
Tues, Nov 1First-Order Logicsyntax, examplesCh. 8
Thurs, Nov 3Homework #3 duemodel theory, ontologies
Tues, Nov 8Inference in FOLunificationCh. 9
Thurs, Nov 10forward-chaining, backward-chaining, and resolution inference in FOLJess, PROLOG (see links below), algs
Tues, Nov 15Limitations of FOL; alternative KR systems defeasible reasoning; negation-as-failure in PROLOG; non-monotonic logics and circumscription; fuzzy logic; semantic nets and inheritance; probability and Bayes Rule9.4.2, 12.5.1, 12.6, 13.1-2; slides
Thurs, Nov 17Project 4 duePROLOG my notes on Prolog
Tues, Nov 22Situation Calculus
Thurs, Nov 24Thanksgiving (class cancelled)
Tues, Nov 29Planninggoal regressionCh. 10 (skip 10.3)
Thurs, Dec 1Intelligent AgentsCh 2, slides
Tues, Dec 6(last day of class); Project #5 dueIBM Watson
Fri, Dec 9final exam, 3:00-5:00 (113 HRBB)

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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