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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 factors-based approaches, probabilistic reasoning methods, the Dempster-Shafer 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 Rule-based Systems. |
(0) [PDF]
(1) [PDF] |
MG |
Basic Knowledge Representation First-Order Logic Rule-Based 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 Pseudo-Probabilities: The PROSPECTOR Approach |
(3) [PDF] |
MG |
(3) References [2, 4, 7] |
Oct 29 |
(3) Reasoning with Pseudo-Probabilities: The PROSPECTOR Approach (cont.)
(4) The Dempster-Shafer Theory of Evidence |
(4) [PDF] |
MG |
(4) References [2, 4, 6] |
Nov 5 |
(4) The Dempster-Shafer Theory of Evidence (cont.)
(5) The Dempster-Shafer 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) R-Fuzzy 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 |
(13-15) 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 |
(16-18) References [8, 9 ,10, 11, 12] |
References: