Artificial Intelligence

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17 Ιουλ 2012 (πριν από 6 χρόνια και 3 μέρες)

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Artificial Intelligence
Andres Mendez-Vazquez
December 16,2009
Room 365
TBD Tuesday and Thursday
9:00 AM - 11:00 AM
Office Hours
After 3:00 PM Room 365
1 Overview
In this class,you will learn to analyze and understand the some of the main
subjects in Artificial Intelligence (AI).
2 Required Text
The required text for this course is:
 S.Russell,N.P.Norvig,“Artificial Intelligence:A Modern Approach (2nd
Edition),” Prentice Hall,2 edition,December 2002.
In addition you must consult the following texts:
 C.M.Bishop,“Pattern Recognition and Machine Learning,” Springer,
October 1 2007.
 S.Haykin,“Neural Networks:Comprehensive Foundation,” IEEE Press,
2nd edition,March 1999.
 R.S.Sutton and A.G.Barto,“Reinforcement Learning:An Introduction”,
MIT Press,Cambridge,MA,1998.
 G.J.Klir,B.Yuan,“Fuzzy Sets and Fuzzy Logic:Theory and Applica-
tions,” Prentice Hall PTR,1st edition,May 1995.
There will also be class notes in pdf format.
3 Prerequisites
Probability,Statistics,Calculus,Linear Algebra
4 Course Requirements
The requirements of the course are
Requirement % of Grade
1.Exam#1 20%
2.Exam#2 20%
3.Exam#3 20%
4.Project 40%
We will use the curve for the final grade.
4.1 Exams
We will have three exams in this course.Each of them will weight 20 points of
your grade.
4.2 Project
Still to be defined.The project must be working at the end of the course.
5 Class Participation
We expect fromyou to read the assigned lectures in advance and make questions!
6 Subjects
6.1 What is Artificial Intelligence?
 A Little bit of history
 Norbert Wiener,John Von Neumann and Company
– The Great expectations times.
– Reality comes knocking.
 Discuss the idea behind the Turing test.
 Why is not used any more by the research community?
 Connectionist point of view.
 Symbolist point of view
 Define and discuss Strong AI vs.Weak AI.
 Searle’s Chinese Room.
 Gödel Incompleteness vs.AI.
 Penrose’s arguments vs the anti-AI
6.2 Intelligent Agents
(a) Why Intelligent Agents?
(b) Using Cooperation to solve problems.
6.3 Searching in AI
1.Classic Techniques
(a) Uninformed Search Strategies.
(b) Partial Knowledge Techniques.
(c) Heuristics functions.
2.Advanced Searching Techniques
(a) Local Optimization - Why local search?
i.Lagrange Multipliers.
ii.Expectation Maximisation.
iii.Steepest Descent - Hill Climbing.
iv.Interior point methods.
(b) Global Optimization - Why global search?
i.Simulated Annealing.
ii.Genetic Algorithms.
iii.Particle Swarm Optimization.
(c) Meta-Heuristics - Why Meta-Heuristics?
i.Ant Swarm Optimization.
ii.Cross-entropy method.
iii.Harmony search.