Foundations of Artificial Intelligence

The course is a substitute for:

Foundations of Artificial Intelligence (E33ZUI)
Foundations of Artificial Intelligence (X33ZUI)

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

This course reviews the basic techniques which can be applied for design and development of intelligent systems. There are introduced and explained principles of AI including state space search, knowledge representation, expert systems for diagnostics and planning, machine learning, natural language processing, machine perception, distributed AI and practical applications of AI.

Requirements:

Conditions for approval: presence in seminars and labs, presentation of solved task

Syllabus of lectures:

1. Artificial Intelligence as a tool for informatics. Motivation. Examples of applications

2. State space as means for problem representation, search in the state space. Production systems

3. Heuristic methods of search. Intelligent search

4. Formalisms of knowledge representation. Exploatation of knowledge in AI systems

5. Predicate calculus. Automated reasoning. Proof using resolution

6. Diagnostic expert systems (ES). 1st and 2nd generation of ES

7. Planning and schedulling. Expert systems for planning, examples of solutions

8. Machine learning. Review of different methods. Learning from examples

9. Inductive methods of learning, practical applications

10. Natural language processing. Man-machine communication

11. Distributed AI (DAI). Multiagent systems, communication using blackboard

12. Types of architecture for DAI, coordination, cooperation and communication. Applications.

13. BDI model. Coalition forming

14. Examples of industrial and medical applications of AI systems

Syllabus of tutorials:

1. Introduction. Simple problems, their representation and solution

2. State space search - task fomalization, basic problem solving strategies

3. Description of heuristic functions to be used in state space search

4. Examples of various types of knowledge representation

5. Resolution principle. Principles of Prolog

6. Prolog as a tool for AI problem solving, namely for search and reasoning

7. Hands on excercises with the expert system FEL-EXPERT

8. Design of knowledge bases for the expert system

9. Debugging and testing of the individually designed knowledge bases

10. Machine learning - representation of the objects

11. Inductive methods of machine learning - hands on excercises

12. Design of a mutiagent system - an example

13. Hands on excercises with the multiagent system

14. Hands on excercises with the multiagent system

Study Objective:

Study materials:

[1] Nilsson,N.J.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann Pub., Inc., San Francisco, 1998

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: