Welcome to the home page of Models
for Inexact Reasoning, a basic course offered by the European Masters
Program in Computational Logic at the School of Computer Science (Universidad Politécnica de Madrid).
Course
Syllabus
Course
Overview:
The automated management of imprecision and its associated uncertainty
are of huge interest in present and future applications of Computational Intelligence.
This course deals with advanced reasoning methods to handle uncertainty and imprecision. This includes certainty factorsbased approaches, probabilistic reasoning methods, the DempsterShafer Theory, and Fuzzy Logic. Fuzzy Logic deals with the management of imprecision emphasizing in the meaning issue, and provides a sound
theoretical framework and a wide range of practical applications. The main
topics in Fuzzy Logic are the
fuzzy sets standard theories, the study of linguistic modifiers and
quantifiers, the approximate inference, and the granularity of the related
imprecise concepts. Together with Neural Networks and Evolutive Algorithms, Fuzzy
Logic is at the very heart of the Soft Computing discipline, one of the most
fruitful areas of Computational Intelligence.
Lecturers:
Locations
and times:
All lectures will be held at Meeting Room #2 of the Department of Artificial Intelligence. Lectures are Thursdays 12 pm  2 pm.
Student personal record forms:
All students must download and fill in the electronic student record form (including a recent photograph) and send it by email to Prof. Miguel García by October 21st, 2009.
Schedule:
Details of the
schedule, slides and reading lists will be updated as the course progresses.
The schedule and the readings are subject to change.
Date  Topics  Slides  Who  Recommended Readings and Additional Material 

Oct 8 
Course Presentation. (0) Introduction to Uncertainty, Imprecision and Approximate Reasoning. (1) Overview of Rulebased Systems. 
(0) [PDF]
(1) [PDF] 
MG 
Basic Knowledge Representation FirstOrder Logic RuleBased Systems Frames Constraints (0, 1) References [1, 2, 4] 
Oct 15 
(2) Reasoning with Certainty Factors. The MYCIN Approach 
(2) [PDF] 
MG 
(2) References [2, 4, 6] 
Oct 22 
(2) Reasoning with Certainty Factors. The MYCIN Approach (cont.)
(3) Reasoning with PseudoProbabilities: The PROSPECTOR Approach 
(3) [PDF] 
MG 
(3) References [2, 4, 7] 
Oct 29 
(3) Reasoning with PseudoProbabilities: The PROSPECTOR Approach (cont.)
(4) The DempsterShafer Theory of Evidence 
(4) [PDF] 
MG 
(4) References [2, 4, 6] 
Nov 5 
(4) The DempsterShafer Theory of Evidence (cont.)
(5) The DempsterShafer Theory of Evidence  A Sample Scenario 
(5) [PDF] 
MG 
(4) References [2, 4, 6] 
Nov 12 
(6) Applications of uncertain reasoning: Information Retrieval
 MG 
(4) References [20] 

Nov 19 
(7) Description of assignment #1 IMPORTANT! Attendance to this lecture is COMPULSORY. 
(7) [Statement] 
MG 
(7) References [20] 
Nov 26 
(8) Introduction to Fuzzy Prolog 
(8) [PDF] 
SM 
(8) References [8, 9, 10] 
Dec 3 
(9) Fuzzy Logic  Lesson 1: Crisp and Fuzzy Sets 
(9) [PDF]
 FB 
(9) References [8, 9 ,10, 11, 12] 
Dec 10 
(10) Fuzzy Logic  Lesson 2: Fuzzy Propositions (11) Fuzzy Logic  Lesson 5: Fuzzy Relations 
(10) [PDF]
(11) [PDF] 
FB 
(10, 11) References [8, 9 ,10, 11, 12] 
Dec 17 
(12) RFuzzy Lecture 
(12) [PDF] 
SM 

Jan 14 
(13) Fuzzy Logic  Lesson 6: Inference from Conditional Fuzzy Propositions (14) Fuzzy Logic  Lesson 7: Fuzzy Expert Systems (15) Fuzzy Logic  Lesson 9: Selection of Fuzzy Implications 
(13) [PDF]
(14) [PDF]
(15) [PDF] 
FB 
(1315) References [8, 9 ,10, 11, 12] 
Jan 21 
(16) Fuzzy Logic  Lesson 3: Fuzzy Quantifiers (17) Fuzzy Logic  Lesson 4: Fuzzy Hedges (18) Fuzzy Logic  Lesson 8: Fuzzy Controllers 
(16) [PDF]
(17) [PDF]
(18) [PDF] 
FB 
(1618) References [8, 9 ,10, 11, 12] 
References: