This document outlines the course details for an "Intelligent Systems" course including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.

1. COURSE “Intelligent Systems”
2005 Fall
Lecturer - Professor, Dr. Andrey V.Gavrilov.
To append for course “Intelligent systems”
Course Description
This course will provide students the basic concepts of different methods and tools
of knowledge engineering for building of intelligent systems based on knowledge,
such as, logic, rules, frames, semantic nets. The course will provide students the
knowledge about applied intelligent systems. In particular, expert systems,
intelligent systems, systems based on natural language processing. This course
contains methodology and technology of development of expert systems,
technologies for building of systems understanding of natural language.
For all other students:
Grading Policy (tentative)
Midterm Final Total
50% 50% 100%
Lecturer - Professor, Dr. Andrey V.Gavrilov.
Course includes 16 lectures and 8 practical works.
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

2. 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/
Date Kind Title Room
13.09. Lecture 1 Introduction in AI. 445
What is AI? History of AI. Role of AI in
informatics and Civilization. Two approaches to
development of AI – Neuroinformatics and
Logical AI.
15.09 Lecture 2 Applied AI Systems. Kinds. Experts systems, 445
Intelligent Robots.
20.09 Lecture 3 Main definitions of Knowledge Engineering. 445

3. Kinds of knowledge representations. Intelligent
agents. Logic for Knowledge representation. 1-
order logic.
22.09 Lecture 4 Solving of tasks in 1-order logic. Introduction to 445
logic programming.
27.09 Lecture 5 Programming in Prolog. 445
29.09 Lecture 6 Semantic nets and frames. Comparison of 445
Knowledge representations and problem of
selection of its. Kinds of combinations of
different methods of KR.
5.10 Lecture 7 Methods of solving of tasks in knowledge-based 445
learning. Searching in state-space, logic
inference, matching, using of context, in
particular, inheritance.
7.10 Lecture 8 Problem of acquisition of knowledge. Kinds of 445
methods. Induction.
11.10 Colloquium 1 Positive and negative of logic in thinking and AI. 445
13.10 Lecture 9 Tools of knowledge engineering. Kinds. 445
Examples.
17.10 Lecture 10 Introduction to programming in Lisp 445
19.10 Lecture 11 Architecture of Expert Systems. Characteristics 445
of Expert Systems. Steps of development of ES.
25.10 Lecture 12 Tools for development of ES. Example of expert 445
shell – ESWin
27.10 Lecture 13 Review of modern Expert Systems 445
1.11 Exam MIDTERM EXAM 445
3.11 Lecture 14 Intelligent robots. Classification of modern 445
robots. Functions of control system. Features of
development of humanoid robots.
8.11 Lecture 15 Review of intelligent robots. 445
10.11 Lecture 16 Example of shell for simulation of mobile robot 445
Webots. Introduction to standart of mobile
robots – JAUS.
15.11 Lecture 17 Control systems of robots based on NN. 445
17.11 Lecture 18 Problems of natural language processing (NLP). 445
22.11 Lecture 19 Methods of simulation of understanding of NL. 445
24.11 Lecture 20 Examples of NLP in searching systems. 445
29.11 Lecture 21 Example of ALICE-like dialog system. 445
Language AIML for ALICE-like systems
1.12 Colloquium 2 Test of Turing and problem of testing of 445
Intelligence.
6.12 Lecture 22 Introduction to recognition of speech. 445
8.12 Lecture 23 Introduction to Intelligent Data Analyzing
(IDA). Example of system for Data Analyzing
based on neural networks.
13.12 Lecture 24 Ontologies. Semantic WEB. 445
15.12 Lecture 25 Multi-agent systems. 445
20.12 Colloquium 3 Future of AI. Dangers and problems of
development of AI.
22.12 Lecture 26 FINAL EXAM 445

4. 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).