Title:

Intelligent Systems

Code:SIN
Ac.Year:2007/2008
Sem:Winter
Curriculums:
ProgrammeField/
Specialization
YearDuty
IT-MSC-2MGM.1stCompulsory
IT-MSC-2MIN.1stCompulsory
IT-MSC-2MIS.1stCompulsory
IT-MSC-2MPS1stCompulsory
Language of Instruction:Czech
Public info:http://www.fit.vutbr.cz/study/courses/SIN/public/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:26100213
 ExamsTestsExercisesLaboratoriesOther
Points:602001010
Guarantor:Janoušek Vladimír, doc. Ing., Ph.D. (DITS)
Lecturer: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, role of simulation in the design and development of intelligent systems, uncertain and incomplete information processing, introduction to softcomputing, agent and multiagent architectures, learning and adaptive systems, reinforcement learning, planing and scheduling, multiagent systems and their applications,  robotic systems, expert systems.
Knowledge and skills required for the course:
  Artificial intelligence basics: Laguages LISP a and Prolog, problem solving, state space search, problem decomposition, machine learning principles.
Modeling and Simulation basics: System, model, simulation, simulation time, discrete event simulation, continuous systems simulation.
Learning outcomes and competencies:
  Students acquire knowledge of principles and design of intelligent systems.
Syllabus of lectures:
 
  1. Introduction
  2. Intelligent systems overview
  3. Model based development of intelligent systems
  4. Introduction to softcomputing
  5. Agent and multiagent architectures 
  6. Learning and adaptive systems
  7. Reinforcement learning
  8. Planing and Scheduling 
  9. Robotic systems
  10. Games theory
  11. Multiagent systems and their applications
  12. Expert systems
  13. Summary
Syllabus - others, projects and individual work of students:
 
  • Individual project - design and implemetation 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. Negnevitsky, M.: Artificial Intelligence, Addison Wesley, 2001, ISBN 0-321-20466-2
  3. Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction, The MIT Press, Cambridge, MA, 1992
  4. Mitchel, T.: Machine Learning. McGraw Hill, 1997
  5. 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. Negnevitsky, M.: Artificial Intelligence, Addison Wesley, 2001, ISBN 0-321-20466-2
  3. Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction, The MIT Press, Cambridge, MA, 1992
  4. Mitchel, T.: Machine Learning. McGraw Hill, 1997
  5. 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
 

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