Subject description - A5M33UIP

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A5M33UIP Advanced Artificial Intelligence Extent of teaching:3P+1C
Guarantors:  Roles:PV Language of
teaching:
CS
Teachers:  Completion:KZ
Responsible Department:13133 Credits:4 Semester:L

Anotation:

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.

Course outlines:

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

Exercises outline:

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

Literature:

[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

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.

Subject is included into these academic programs:

Program Branch Role Recommended semester
MPIB Common courses PV 2


Page updated 13.12.2019 17:52:09, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)