Soft Computing, 2005 Fall

Soft Computing

Autumn 2005

Monday and Wednesday 16:30 ~ 17:45

Electronic & Information Building #107

last updated: 2005/12/22

1. Course Description

This course will provide students the basic concepts of different methods and tools for processing of uncertainty in intelligent systems, such as, fuzzy models, neural networks, probabilistic models, and foundations of its using in real systems. The course cover main concepts of philosophy of AI and hybrid intelligent systems, classification and architecture of hybrid intelligent systems.

2. Textbooks

* Presentation materials [HERE]

* Contents of lectures:

1. Introduction

What is Soft Computing (SC)?

Necessary of Representation of Fuzzy Things in AI

Two approaches to development of AI – Neuroinformatics and Logical AI

2. Two-level model of mind

Two levels – associative (images) and logical (signs)

Model of associative thinking

Analogy and associations

Tasks of recognition, classification and clusterization of images

Two approaches of improvement of logical and neural models – “From logic to fuzziness” (up-down) and “From neural networks to concepts” (down-up)

3. From logic to fuzziness

Fuzzy Sets

Fuzzy Logic

Linguistic Variables

Pseudo-Physics logics

Definitions

Space Pseudo-Physics logic

Temporal Pseudo-Physics logic

Problem of learning of intelligent systems based on fuzzy sets

4. From neural networks to concepts

Main definitions

Classification of Neural Networks

Kinds of tasks for solving by NN

Kinds of Training of NN

Perceptron and Error Back Propagation Algorithm

Hopfild’s network and Hebb’s training algorithm

Multi-Layer recurrent neural networks

SOM of Kohonen

Reinforcement learning

Constructive algorithms of training of neural networks

Hamming’s neural network

ART of Grossberg-Carpenter

Constructive algorithms of training for MLP

5. Probabilistic methods of Knowledge Representation and Reasoning

Bayes’s nets

Genetic Algorithms

6. Using of neural networks in NLP

6. Paradigm of Hybrid Intelligent Systems (HIS)

Motivation

Kinds of architectures

Fuzzy neural networks

Review of HIS

7. Introduction to generalized theory of uncertainty

8. Conclusion. Modern trends in development of soft computing

3. Grading Police (tentative)

Midterm	Final	Total
50%	50%	100%

4. Instructor

Name

Andrey Gavrilov (Russia)

Contact Information

Tehephone :	031-201-2493
E-mail :	avg@oslab.khu.ac.kr
Office :	Electronic & Information Building #B08

_material

5. Tentative Schedule (updating)

#	Kind	Topic	Material
1	Lecture 1	Introduction. What is Soft Computing (SC)? Necessary of Representation of Fuzzy Things in AI. Two approaches to development of AI – Neuroinformatics and Logical AI.	DOWN
2	Lecture 2	Two-level model of mind. Two levels – associative (images) and logical (signs). Model of associative thinking. Analogy and associations. Tasks of recognition, classification and clustering of images. Two approaches of improvement of logical and neural models – “From logic to fuzziness” (top-down) and “From neural networks to concepts” (bottom-up).	DOWN
3	Lecture 3	Fuzzy Sets. Fuzzy Logic	DOWN
4	Colloquium 1	Problems of simulation of mind.	DOWN
5	Lecture 4	Linguistic Variables. Pseudo-Physics logics Definitions. Space Pseudo-Physics logic. Temporal Pseudo-Physics logic.	DOWN
6	Lecture 5	Using of Fuzzy logic in control systems.	DOWN
7	Lecture 6	Introduction in neural networks. First models of NN. Tasks solved by NN. Kinds of learning of NN.	DOWN
8	Lecture 7	Multilayer perceptrons (MLP). Tasks of regression and classifications. Algorithm error back propagation.	DOWN
9	Lecture 8	Using of perceptron for image recognition and forecasting.	DOWN
10	Lecture 9	Associative memory based on Hopfield model	DOWN
11	Lecture 10	Boltzmann Machine	DOWN
12	Lecture 11	Multi-layer recurrent neural networks.	DOWN
13	Lecture 12	Self-organized map (SOM) of Kohonen	DOWN
14	Lecture 13	Reinforcement learning and it’s using in robotics.	DOWN
15	Lecture 14	Constructive algorithms of learning of NN.	DOWN
16	Lecture 15	Hemming’s Newral Networks.Constructive algorithms of learning for MLP.	DOWN
17	Lecture 16	Introduction to spike neuron models. Tools for developments of neural networks	DOWN
18	Colloquium 2	How to select of neural model for solving of task? Hybrid neural networks.	DOWN
19	Exam	MIDTEAM EXAM	PREPARE
20	Lecture 17	Introduction to probabilistic reasoning. Bayes’s nets. Markov’s model.	DOWN
21	Lecture 18	Foundations of genetic algorithms (GA). Using of GA.	DOWN
22	Lecture 19	Motivation and main definitions of Hybrid intelligent systems (HIS). Kinds of architectures. Fuzzy neural networks. Two-hemisphere architecture.	PART_I PART_II
23	Lecture 20	Review of HIS.	DOWN
24	Colloquium 3	How select of hybrid architecture for solving of task?
25	Lecture 21	Review of using of NN for solving of real tasks.	DOWN
26	Lecture 22	Using of NN in NLP and speech recognition	DOWN
27	Lecture 23	Hardware neural networks	DOWN
28	Lecture 24	Future of soft computing. Introduction to generalized theory of uncertainty	DOWN
29	Exam	FINAL EXAM

6. Suggested reading

1. Arbib, M. A. The Metaphorical Brain. New York: Wiley (1990).

2. Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing. Seppo J. Ovaska (Editor). (Electronic contents and Chapter 1 are available).

3. Duda R.O., Hart P.E., Stock D.G. Pattern Classification, John Wiley (2001).

4. Honavar, V. & Uhr, L. (Ed.) Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. San Diego, CA: Academic Press (1994).

5. Hristev R.M. Artificial Neural Networks. (Electronic book is available).

6. Jones M.T. AI application Programming. Charles River Media, Inc., Hingham, Massachusetts (2003).

7. Kecman V. Learning and Soft Computing. MIT, 2001. (Electronic book is available).

8. Koivo H. Soft Computing in dynamical systems. (Electronic book is available).

9. Krose B., van der Smagt P. An Introduction to Neural Networks. 1996 (Electronic book is available).

10. Lawrence J. Introduction to Neural Networks. California Scientific, Nevada City, (1993).

11. Luger G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison Wesley (2002).

12. Mitchell T.M. Machine learning. McGraw Hill, 1997.

13. Nillson N.J. Introduction to Machine Learning. Draft of textbook. (Electronic book is available).

14. Russel, S. & Norvig, P., Artificial Intelligence - a modern approach (2th edition). Englewood Cliffs, NJ: Prentice Hall (2002). (Electronic contents and ?hapters 5,7,11,20 are available).

15. Sutton R.S., Barto A.J. Reinforcement Learning: An Introduction. The MIT Press Cambridge, Massachusetts, London. (Electronic book is available).

16. Wasserman, P. Advanced Methods in Neural Computing. New York: Van Nostrand Rheinhold (1993).

17. Wilson R.A. Quantum Psychology. 1990.

7. Download the syllabus file (MS-WORD) : [DOWN]