Artificial Intelligence

Relations:

It is not possible to register for the course AE3M33UI if the student is concurrently registered for or has already completed the course A3M33UI (mutually exclusive courses).

During a review of study plans, the course A3M33UI can be substituted for the course AE3M33UI.

It is not possible to register for the course AE3M33UI if the student is concurrently registered for or has already completed the course B3M33UI (mutually exclusive courses).

It is not possible to register for the course AE3M33UI if the student is concurrently registered for or has previously completed the course A3M33UI (mutually exclusive courses).

It is not possible to register for the course AE3M33UI if the student is concurrently registered for or has previously completed the course B3M33UI (mutually exclusive courses).

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

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.

Requirements:

Syllabus of lectures:

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

Syllabus of tutorials:

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

Study Objective:

Study materials:

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

Note:

Further information:

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

No time-table has been prepared for this course

The course is a part of the following study plans: