Advanced Artificial Intelligence
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
A5M33UIP | KZ | 4 | 3P+1C | Czech |
- 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: