CSCE 625 - Artificial Intelligence

Spring 2015

Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 458-5518
email:ioerger@cs.tamu.edu
office hours: Wed, 11:10-12:00 (after class)

TA: Eric Nelson
email:ejn8411@tamu.edu
office: RDMC-B021
office hours:Tues, 2:00-3:00

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

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs625-spr15/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 11 programming assignments. There will be no exams. The final grade for the course will be determined from the weighted-average of assignment scores 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:

Programming Assignments:
P1due Feb 2Searchcompare DFS, BFS, and BestFirst on map navigation P1 handout ; ATM.graph data file
P2due Feb 13A*implement a heuristic to solve Blocksworld problemsP2 handout
P3due Feb 23SAuse Simulated Annealing to solve TSP for tour of TexasP3 handout, input file, mapping tool
P4due Mar 4CSPbacktracking search with MRV heuristic P4 handout
P5due Mar 13Game AIOthello competition using Minimax search (+ board eval function)P5 handout, play online, rules of othello
P6due Mar 25Resosolve Sammy's Sport Shop in propositional logic using resolution refutationP6 handout
P7due Apr 6DPLLBoolean satisfiability solver, use to solve Farmer-Fox-Chicken-Grain problem via SatPlanP7 handout
P8due Apr 24Prologpractice problemsP8 handout
P9due May 8GoalReggoal-regression planner for BlocksworldP9 handout

assignmenttopicconceptsreading
Wed Jan 21(first day of class)What is AI?perspectives on AICh. 1
Fri, Jan 23Search AlgorithmsDFS, BFS, BestFirst, UC, ID, A*Ch. 3
Mon, Jan 26
Wed, Jan 28
Fri, Jan 30
Mon, Feb 2P1 due (Search)Iterative improvementhill-climbing, simulated annealingCh. 4
Wed, Feb 4
Fri, Feb 6
Mon, Feb 9Game-Playingminimax, alpha-beta pruningCh. 5
Wed, Feb 11
Fri, Feb 13P2 due (A*)
Mon, Feb 16Constraint-Satisfactionback-tracking, MRV, AC-3Ch. 6
Wed, Feb 18
Fri, Feb 20
Mon, Feb 23P3 due (SA) Propositional Logic syntax, semantics
propositional inference algs:
natural deduction 		Ch. 7
Wed, Feb 25
Fri, Feb 27
Mon, Mar 2resolution refutation proof procedure
Wed, Mar 4P4 due (CSP)
Fri, Mar 6satifiability algs: DPLL, WalkSat
Mon, Mar 9class cancelled
Wed, Mar 11in-class help session, pre-competition
Fri, Mar 13P5 due (othello)
Mon, Mar 16(Spring Break)
Wed, Mar 18in-class help session
Fri, Mar 20(Spring Break)
Mon, Mar 23First-Order Logicsyntax, semantics
Ch. 8
Wed, Mar 25P6 due (reso)
Fri, Mar 27FOL inference methodsCh. 9
Mon, Mar 30
Wed, Apr 1
Fri, Apr 3class cancelled (reading day)
Mon, Apr 6P7 due (DPLL)unification
Wed, Apr 8resolution in FOL, Herbrand's Theorem
Fri, Apr 10back-chaining
Mon, Apr 13Prolog
Wed, Apr 15arithemetic in Prolog
Fri, Apr 17negation in Prolog
Mon, Apr 20Defaults and Exceptions
Wed, Apr 22PlanningSituation CalculusCh. 10
Fri, Apr 24P8 due (Prolog)Frame Problem
Mon, Apr 27STRIPS operators, Goal RegressionWeld (1994)
Wed, Apr 29Uncertainty (probability)Bayes Rule, conditional independence, utilityCh. 13
Fri, May 1Bayesian networksexact inference, belief propagation, HMMsCh. 14, slides
Mon, May 4
Tues, May 5last day of class (redefined day)
Fri, May 8P9 due (Goal Regr)
Mon, May 11No final exam

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

Americans with Disabilities Act (ADA) Policy Statement

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services, in Cain Hall, Room B118, or call 845-1637. For additional information visit http://disability.tamu.edu.

Links