Subject description - AE3M33UI

Summary of Study | Summary of Branches | All Subject Groups | All Subjects | List of Roles | Explanatory Notes               Instructions
AE3M33UI Artificial Intelligence Extent of teaching:2P+2C
Guarantors:  Roles:PO,V Language of
teaching:
EN
Teachers:  Completion:Z,ZK
Responsible Department:13133 Credits:6 Semester:L

Anotation:

The course is aimed at providing theoretically deeper knowledge in the area of Artificial Intelligence in the extent needed to study the branch of study Robotics. It is organized around several topics: pattern recognition and machine learning, theory of multi-agent systems and artificial life. The linkage between the theoretical and practical applications is rather stressed.

Course outlines:

1. Classification methods, Bayesian and non-Bayesian tasks
2. Adaboost, SVM classifiers
3. Graphical probabilistic and Markov models in machine learning
4. Theory of learning, problems of consistency, capacity, PAC
5. Learning of classification rules (AQ, CN2)
6. Sequential pattern recognition, Walds algorithm, extraction and synthesis of features, properties
7. Planning, representation of the planning problem, linear and non-linear planning
8. Methods of planning: TOPLAN, POPLAN, SATPLAN, GRAPHPLAN
9. Multi-agent systems: Reactive and deliberative agents, BDI architecture, reflection
10. Collective behavior of agents, distributed decision making, negotiation techniques, CNP, auction and voting techniques
11. Social knowledge, social behavior of agents, met-reasoning, coalition formation, team cooperation
12. Multi-agent planning and scheduling, industrial applications
13. Artificial life, principles, algorithms, applications
14. Applications

Exercises outline:

1. Introduction, definition of the course project
2. Bayesian and non-Bayesian tasks
3. Adaboost and SVM classifiers demos of tasks
4. Markov models and machine learning I 5.Markov models and machine learning II
6. AQ and CN2 systems, experiments I 7.AQ and CN2 systems, experiments II
8. Planning tasks
9. Planning - practical exercise
10. Aglobe Systems and its features, demo
11. Demos of multi-agent systems (Agentfly, ProPlant, MAST)
12. Agentification of systems, semantic information
13. Artificial life demos
14. Delivery of course project

Literature:

1. Wooldridge, M.: An Introduction to Multi-Agent Systems, John Wiley & Sons, 2002
2. Nilsson N.J. & Nilsson, N.J.: Artificial Intelligence: A New Synthesis. Elsevier Science, 1998

Requirements:

Webpage:

http://cw.felk.cvut.cz/doku.php/courses/ae3m33ui/start

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEKME1 Wireless Communication V 2
MEKME5 Systems of Communication V 2
MEKME4 Networks of Electronic Communication V 2
MEKME3 Electronics V 2
MEKME2 Multimedia Technology V 2
MEOI1 Artificial Intelligence V 2
MEOI5NEW Software Engineering V 2
MEOI5 Software Engineering V 2
MEOI4 Computer Graphics and Interaction V 2
MEOI3 Computer Vision and Image Processing V 2
MEOI2 Computer Engineering V 2
MEEEM1 Technological Systems V 2
MEEEM5 Economy and Management of Electrical Engineering V 2
MEEEM4 Economy and Management of Power Engineering V 2
MEEEM3 Electrical Power Engineering V 2
MEEEM2 Electrical Machines, Apparatus and Drives V 2
MEKYR1 Robotics PO 2


Page updated 5.12.2019 17:52:22, 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)