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Intelligent System

Autumn 2005

Tuesday & Thursday 16:30 ~ 17:45

Electronic & Information Building  #445

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 in AI

             History of AI

             Role of AI in informatics and Civilization

             Two approaches to development of AI Neuroinformatics and Logical AI

2. Features of Kinds of Applied AI Systems

             Experts systems

             Intelligent Robots

             Natural language Processing

4. Main definitions of Knowledge Engineering

5. Methods of Knowledge Representation and Solving of tasks

             Space of states and search in it

             Predicate Logic

             Fuzzy Sets and Fuzzy Logic

             Pseudo-Physics logics

             Rules

             Semantic Nets

             Frames

Soft Computing

6. Comparison and Selection of different Methods of Knowledge Representation

7. Languages and tools for Knowledge Engineering

             Introduction to programming in PROLOG

             Features of LISP

             KR-languages

8. Expert Systems

             Conditions for efficient use of Expert Systems

             Steps of development of Expert System

             Features of architectures of Expert Systems

9. Machine learning

             Induction

             Neural networks

             Reinforcement learning

             Genetic algorithms

10. Hybrid Intelligent Systems

          Combination of knowledge representation methods,

          Review of Hybrid Expert Systems (HES),

          Classification of HES,

          Architecture of ESWin (toolkit for building of HES)

11. Paradigm of Distributed (multi-agent) Intelligent Systems

12. Paradigm of Ontology and Semantic WEB

13. Intelligent Robots

             Classification of robots and its control systems

             Functions of control systems of Robots

             Task solving by mobile robots

             Features of Development of Humanoid Robots

14. Natural language processing

             Problems

             Kinds of NLP-systems

            Review of methods of NLP,

            Review of use of neural networks in NLP systems,

Architecture of learning software for search of documents by query on Natural Language

Introduction to speech recognition  

15.  Conclusions. The future of Artificial Intelligence and Applied AI Systems,

         

Practical works are based on original products, developed by author and his colleagues:

-         ESWin,  software for building of hybrid expert systems,

-         Software Alang+Finder for search of documents by query on Natural Language,

-         Software AnalDB for Data Base Analyzing by Neural Networks (perceptron + Hopfield model).

More information on this software is available from http://www.insycom.ru and http://ermak.cs.nstu.ru/islab/

 

 

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 (tentative)

#

Date

(Month/Day)

Kind

Title

Material

1

9/13

Lecture 1

Introduction in AI.

What is AI? History of AI.  Role of AI in informatics and Civilization. Two approaches to development of AI Neuroinformatics and Logical AI.

PPT

 

9/15

Lecture 2

Applied AI Systems. Kinds. Experts systems, Intelligent Robots.

PART1

PART2

2

9/20

Lecture 3

Main definitions of Knowledge Engineering. Kinds of knowledge representations. Intelligent agents. Logic for Knowledge representation. 1-order logic.

PART1

PART2

 

9/22

Lecture 4

Solving of tasks in 1-order logic. Introduction to logic programming.

 

3

9/27

Lecture 5

Programming in Prolog.

PPT

 

9/29

Lecture 6

Semantic nets and frames. Comparison of Knowledge representations and problem of selection of its. Kinds of combinations of different methods of KR.

PPT

4

10/5

Lecture 7

Methods of solving of tasks in knowledge-based learning. Searching in state-space, logic inference, matching, using of context, in particular, inheritance.

PPT

 

10/7

Lecture 8

Problem of acquisition of knowledge. Kinds of methods. Induction.

PPT

5

10/11

Colloquium 1

Positive and negative of logic in thinking and AI.

PPT

 

10/13

Lecture 9

Main concepts of fuzzy logic and linguistic variables, LISP

PPT

6

10/17

Lecture 10

Development of Expert Systems

PPT

 

10/19

Lecture 11

Architecture of Expert Systems. Characteristics of Expert Systems. Steps of development of ES.

PPT

7

10/25

Lecture 12

Introduction to Hybrid Expert Systems, Review of ES

PPT

 

10/27

Lecture 13

Review of modern Expert Systems

PPT

8

11/1

Exam

MIDTERM EXAM

 

 

11/3

Lecture 14

Intelligent robots. Classification of modern robots. Functions of control system. Features of development of humanoid robots.

 

9

11/8

Lecture 15

Review of intelligent robots.

 

 

11/10

Lecture 16

Example of shell for simulation of mobile robot Webots. Introduction to standart of mobile robots JAUS.

Webot Guide

10

11/15

Lecture 17

Control systems of robots based on Neral Networks.

PPT

 

11/17

Lecture 18

Problems of natural language processing (NLP).

PPT

11

11/22

Lecture 19

Methods of simulation of understanding of NL.

PPT

 

11/24

Lecture 20

Examples of NLP in searching systems.

PPT

12

11/29

Lecture 21

Example of ALICE-like dialog system. Language AIML for ALICE-like systems

PPT

 

12/1

Colloquium 2

Test of Turing and problem of testing of Intelligence.

PPT

13

12/6

Lecture 22

Introduction to recognition of speech.

PPT

 

12/8

Lecture 23

Introduction to Intelligent Data Analyzing (IDA). Example of system for Data Analyzing based on neural networks.

PPT

14

12/13

Lecture 24

Ontologies. Semantic WEB.

PPT

 

12/15

Lecture 25

Multi-agent systems.

PPT

15

12/20

Colloquium 3

Future of AI. Dangers and problems of development of AI.

PPT

 

12/22

Exam

FINAL EXAM  

 

6. Suggested reading

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

2.  Bradshaw, J. (Ed.) Software Agents. Cambridge, MA: MIT Press (1997).

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

4.  Jackson P. Introduction to Expert Systems. Addison Wesley, Publishing Company, Inc. (1998).

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

6.  Luger G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison Wesley  (2002). (Electronic content of separate parts is available).

7.  Minsky, M. Society of Mind. New York: Basic Books (1986).

8.  Mitchell T. Machine Learning. MacGraw Hill (1997).

9.  Nilsson, N., The Mathematical Foundations of Learning Machines, San Francisco: Morgan Kaufmann (1990).

10. Nilsson, N., Principles of Artificial Intelligence, San Francisco: Morgan Kaufmann (1980).

11. Newell, A. Unified Theories of Cognition. Cambrdge, MA: Harvard University Press (1990).

12. Russel, S. & Norvig, P., Artificial Intelligence - a modern approach. Englewood Cliffs, NJ: Prentice Hall (2002). (Electronic content of separate parts is available).

13. Sowa J. Knowledge Representation: Logical, Philosophical and Computational Foundation. Pacific Grove, CA: Brooks/Cole (2000).

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

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


 

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

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