Advanced Artificial Intelligence

Relations:

It is not possible to register for the course A5M33UIP if the student is concurrently registered for or has already completed the course A3M33UI (mutually exclusive courses).

The requirement for course A5M33UIP can be fulfilled by substitution with the course A3M33UI.

It is not possible to register for the course A5M33UIP if the student is concurrently registered for or has previously completed the course A3M33UI (mutually exclusive courses).

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

The aim of the course is to provide an overview of advanced

methods used at development of intelligent systems. The

following topics are discussed: advanced methods of state

space search, machine learning, data mining, nature

inspired algorithms (PSO, ACO, evolutionary algorithms,

artificial life), multiagent systems, and their

applications.

Requirements:

For successful completion of the course, it is necessary to

present the results of the individual work to other

students and explain the approaches used.

Syllabus of lectures:

1. Nature of data, information and knowledge. Introduction

to advanced methods of state space search.

2. Methods of state space search (island-driven search,

hierarchical search, limited-horizon search, alpha-beta

search, game strategies)

3. Machine learning - overview of classical methods

4. Multiple classifiers, ILP, relational logic

5. Operators of generalization and specialization,

generalization theory

6. PAC learning, reinforcement learning

7. Application of machine learning to classification,

prediction and other areas

8. Data mining - methods, visualization, applications,

learning of associative rules

9. Distributed methods in learning and optimization

10. PSO, ACO, cellular automata, artificial immune systems,

artificial life

11. Agent - definition, types and properties, models of

architecture (BDI, 3bA), social behaviour

12. Coordination, cooperation and communication in

multiagent systems

13. Models of cooperation (negotiations, market and auction

mechanisms)

14. Planning, alliances, coalition formation, examples of

architecture (BDI, 3bA), social behaviour

12. Coordination, cooperation and communication in

multiagent systems

13. Models of cooperation (negotiations, market and auction

mechanisms)

14. Planning, alliances, coalition formation, examples of

architecture (BDI, 3bA), social behaviour

12. Coordination, cooperation and communication in

multiagent systems

13. Models of cooperation (negotiations, market and auction

mechanisms)

14. Planning, alliances, coalition formation, examples of

applications

Syllabus of tutorials:

1.-3. Advanced algorithms of state space search

4.-9. Machine learning - Weka, programming of designed

algorithm, experiments with real data, comparison of

results acquired using various algorithms

10.-11. Experiments with PSO, ACO

12.-14. Multiagent systems - JADE, work with existing

systems, Aglobe platform

Study Objective:

Study materials:

[1] Wooldridge M., Jennings N.: Intelligent Agents: Theory

and Practice. The Knowledge Engineering Review, 10 (1995), No.2, pp. 115-1526

[2] Dorigo, M., V. Maniezzo, and A. Colorni. "The Ant

System: optimization by a Colony of Cooperating Agents."

IEEE Trans. Syst. Man Cybern. B 26 (1996): 29-41

[3] Russell, S., Norvig, P.: Artificial Intelligence, A

Modern Approach, Prentice Hall Series in AI. New Jersey, Englewood Cliffs, 1995

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: