Title:

Intelligent Systems

Code:SIN
Ac.Year:2009/2010
Term:Winter
Curriculums:
ProgrammeBranchYearDuty
IT-MSC-2MBI1stCompulsory-Elective - group I
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Compulsory-Elective - group M
IT-MSC-2MGM.1stCompulsory
IT-MSC-2MIN2ndCompulsory
IT-MSC-2MIN.1stCompulsory
IT-MSC-2MIS-Compulsory-Elective - group S
IT-MSC-2MIS.1stCompulsory
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPS1stCompulsory
IT-MSC-2MPV1stCompulsory-Elective - group B
IT-MSC-2MSK2ndCompulsory
IT-MSC-2EITE1stCompulsory
Language:Czech
Public info:http://www.fit.vutbr.cz/study/courses/SIN/public/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:26100213
 ExaminationTestsExercisesLaboratoriesOther
Points:70150015
Guarantee:Janoušek Vladimír, doc. Ing., Ph.D., DITS
Lecturer:Janoušek Vladimír, doc. Ing., Ph.D., DITS
Instructor:Janoušek Vladimír, doc. Ing., Ph.D., DITS
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Substitute for:
Artificial Intelligence (UIN), DITS
 
Learning objectives:
To acquaint students with theory and principles of intelligent systems.
Description:
Intelligent system, intelligent systems modeling, simulation in the design of systems, uncertain and incomplete information processing, introduction to softcomputing, agent and multiagent architectures, learning and adaptive systems, reinforcement learning, planing and scheduling, applications.
Knowledge and skills required for the course:
Artificial intelligence basics: Problem solving, state space search, problem decomposition.
Modeling and Simulation basics: System, model, simulation, simulation time, discrete event simulation, continuous systems simulation.
Learning outcomes and competences:
Students acquire knowledge of principles and design of intelligent systems.
Syllabus of lectures:
  1. Introduction. Intelligent systems overview
  2. Agent architectures
  3. Simulation modeling in the development of intelligent systems
  4. Fuzzy logic and fuzzy control
  5. Learning systems. Neural networks
  6. Genetic algorithms. Genetic programming
  7. Markov decision process, reinforcement learning
  8. Planing and Scheduling 
  9. Games theory
  10. Robotic systems
  11. Multiagent systems
  12. Selected applications
  13. Summary
Syllabus - others, projects and individual work of students:
  1. Individual project - simulation model of a simple intelligent system
Fundamental literature:
  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction, The MIT Press, Cambridge, MA, 1992
  3. Mitchel, T.: Machine Learning. McGraw Hill, 1997
  4. Zeigler, B.P.: Theory of Modeling and Simulation, Academic Press; 2 edition (March 15, 2000), ISBN 978-0127784557
Study literature:
  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction, The MIT Press, Cambridge, MA, 1992
  3. Mitchel, T.: Machine Learning. McGraw Hill, 1997
  4. Zeigler, B.P.: Theory of Modeling and Simulation, Academic Press; 2 edition (March 15, 2000), ISBN 978-0127784557
Progress assessment:
  • Mid-term written test
  • PC lab
  • Individuální project