Study programmes / C-SE System engineering and informatics / Artificial Intelligence II
Course code:VUI2
Course title in language of instruction:Umělá inteligence II
Course title in Czech:Umělá inteligence II
Course title in English:Artificial Intelligence II
Mode of completion and number of credits:Exam (6 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes:full-time, 2/2
(full-time, hours of lectures per week / hours of seminars per week)
Language of instruction:English
Level of course:master continuing
Semester:WS 2018/2019 - FBE
Name of lecturer:Ing. Jan Kolomazník, Ph.D. (supervisor)
Prerequisites:Final Bachelor Exam
 
Aims of the course:Students will learn solving complex tasks by searching state space and using undeterministic methods. Students will familiarize with problems of complex optimization tasks and possibilities of solving tasks that have no exact solution.
Course contents:
1.Methods of game playing, problem decomposition into subproblems (allowance 6/10)
 
a.Problems of solving NP-hard and NP-complete tasks
b.State-space searching, AND-OR graphs
c.The design and implementation of the state-space object-representation
d.State-tree pruning methods, heuristic and partial searching
e.Application methods of sate-space searching, solution of a selected problem

2.Additional methods of and approaches to passing through state-space (allowance 10/8)
 
a.Informed searching
b.Searching by competition
c.Planning
d.Complex decisions
e.Knowledge representation, uncertain knowledge and reasoning, heuristics

3.Intelligent agents (allowance 6/4)
 
a.Intelligent agents and methods of their activity
b.Communication between agents and interaction with environment
c.Multi-agent systems and self-learning agents
d.Possibilities of intelligent-agent applications

4.Evolution algorithms (allowance 4/4)
 
a.Specific properties of stochastic and parallel algorithms
b.Evolution strategy
c.Genetic algorithms and genetic programming
d.Model optimization tasks

Type of course unit:optional
Year of study:Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement:There is no compulsory work placement in the course unit.
Recommended study modules:none
Assessment methods:Students have two possibilities to finish this subject - credit or credit and examination.
The condition for obtaining credit is the development work (5 programming miniprojects) on a given topic of the subject matter
Written examination (1 hour), it is necessary to get at least 50% for answers to questions.
 
Learning activities and study load (hours of study load)
Type of teaching methodDaily attendance
Direct teaching
     lecture28 h
     practice28 h
     project work28 h
Self-study
     preparation for exam40 h
     preparation for regular assessment20 h
     preparation for regular testing24 h
Total168 h

Basic reading list
  • OŠMERA, P. Genetické algoritmy a jejich aplikace. Habilitation thesis. FSI VUT, 2002.
  • ŠTĚPÁNKOVÁ, O. -- MAŘÍK, V. -- LAŽANSKÝ, J. Umělá inteligence 1-4. 2nd ed. Praha: Academia, 2003. ISBN 80-200-0502-1.
Recommended reading list
  • KVASNIČKA, V. -- POSPÍCHAL, J. Evolučné algoritmy. Bratislava: STU, 2000. 215 p. ISBN 80-227-1377-5.
  • RUSSELL, S. -- NORVIG, P. Artificial Intelligence: A Modern Approach (2nd Edition). 2nd ed. Prentice Hall, 2002. 1132 p. ISBN 0-13-790395-2.