Artificial Intelligence M.C. Juan Carlos Olivares Rojas Course Syllabus jolivares@uvaq.edu.mx January, 2009
Outline • Introduction • Topics • Grading • Recommendations • References
Introduction
The students will know in detailed form the construction and working of intelligent systems The students will programming and knows diferent kind of languages and applications for artificial intelligence
Basic Concepts 1.1 Basic Concepts 1.2 Applications 1.3 The Intelligent Systems and Learning 1.4 Semantic Networks 1.5 Match and Description Method 1.6 Analogy Problem 1.7 Abstraction Recognition 1.8 Knowledge Interpretation
Networks and Basic Search 2.1 Blind Methods 2.2 Heuristic Method 2.3 The Best Path 2.4 Redundant Paths 2.5 Trees and Search with Adversary Algorithmic Methods 2.6 Trees and Search with Adversay Heuristic Methods
Logic 3.1 Knowledge Representation 3.2 Preposition Logic 3.3 Predicate Logic 3.4 Automatic Deduction
Rules and Chained Rules 4.1 Deduction Systems 4.2 Reaction Systems 4.3 Progressive and Regressive Chained 4.4 Cognitive Modeling 4.5 Problem Solving Models
Lisp Programming
5.1 Introduction 5.2 Structures 5.3 Basic Operations 5.4 Control Structure 5.5 Mathematic Operations 5.6 Predicate Function 5.7 Relations and Sets 5.8 Input and Outputs 5.9 Implementation 5.10 Backup and Recovery
Grading
• Only two partial and one Final Exams (only the last partial). • 70% Partial Exam • 30% Homeworks and Practices • 10% Quizzes
Recommendations
• The classes begin at 19 to 21 hours at 6C Classroom on Tuesday and at Electronic Lab on Thursday • The advisory should be by E-mail, Instant Messenger or by another electronics media • E-mail: jolivares@iuvaq.mx • MSN: juancarlosolivares@hotmail.com • Skype: juancarlosolivares • Web:http://dsc.itmorelia.edu.mx/jcolivares
Recommendations
• The homework must be delivery in Classroom or before class throught moodle or by CD. • The rubric of work contains: – Cover – Abstract – Introducction – Development* – Conclusions – References**
References Nilsson, N. (2001). Artificial Intelligence. A New Synthesis. McGraw-Hill. Russel, S. and Norving, P. (2004). Artificial Intelligece. A New Approach. Pearson Prentice Hall. Winston, P. (1998). Artificial Intelligence. 3rd. Ed., Adisson-Wesley.
References Knight, R. (1997). Artificial Intelligence. 2nd Ed. McGraw-Hill. Tanimoto, S. (1998). The Elements of Artifical Intelligence using Common LISP . W. H. Freeman Company.
Questions?