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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Advanced Artificial Intelligence

The course is not on the list Without time-table
Code Completion Credits Range Language
A5M33UIP KZ 4 3+1c Czech
The course cannot be taken simultaneously with:
Artificial Intelligence (A3M33UI)
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:
Data valid to 2019-03-20
For updated information see http://bilakniha.cvut.cz/en/predmet1134306.html