Artificial Intelligence 1

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

The course covers main topics of symbolic AI, i.e. those closely bound to knowledge representation in logic. First, there are reviewed principles of declarative programming, namely Prolog - a language designed for AI problem solving. Characteristic programming methodology is introduced using first specific solutions of typical general AI problems (state-space search, simple expert system, etc.) and later of very specialized tasks (communication with a computer in natural language and to methods of common sense reasoning). Special attention is given to constraint logic programming and its engineering applications. Finally, inductive logic programming is explained as a new perspective method extending significantly application possibilities of machine learning.

Requirements:

Syllabus of lectures:

1. Programming tools for AI and their requested properties

2. Principles of declarative programming languages. Logic programming and proof by resolution

3. Principles of Prolog

4. State space search and Prolog

5. Review of Prolog solutions for characteristic AI problems. Typical applications

6. New directions in logic programming. Constraint logic programming (CLP)

7. Utilization of natural language communication within AI systems. Phases of natural language processing

8. Role of syntax and semantics of .a sentence. DCG grammars

9. Design of a module for natural language communication. Importance of implicit knowledge. State space search methods and their complexity

10. Common sense reasoning and its partial automation. Naive physics

11. Qualitative simulation

12. Background knowledge and its role in inductive logic programming (ILP)

13. Principles of ILP systems, typical set of training examples and its characteristics

14. Practical applications of ILP

Syllabus of tutorials:

1. Programming language Prolog and its philosophy

2. Facts, rules, queries. List and basic operations on them

3. Negation in Prolog. Implementation of various types of search

4. Hands on exercises with more complex tasks in Prolog I.

5. Grammars in Prolog

6. Hands on exercises with more complex tasks in Prolog II.

7. Hands on exercises with more complex tasks in Prolog III.

8. CLP system Eclipse demonstration

9. Natural language processing in Prolog

10. Implementation of a question-answering systém

11. Common sense reasoning and naive physics

12. System QUASIMODO - hands on exercise

13. Introduction of an ILP system FOIL

14. Hands on exercise with FOIL

Study Objective:

Study materials:

[1] Bratko, I.: Prolog Programming for AI, Reading, Addison Wesley 1991, 2nd edition

[2] Russell, S., Norvig, P.: Artificial Intelligence, A Modern Approach, Prentice Hall Series in AI, Englewood Cliffs, New Jersey, 1995

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Cybernetics and Measurements - Artificial Intelligence- structured studies (compulsory course)