Faculty of Information Technology, BUT

Course details

Intelligent Systems

SIN Acad. year 2007/2008 Winter semester 5 credits

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.

Guarantor

Language of instruction

Czech

Completion

Examination (written)

Time span

26 hrs lectures, 10 hrs exercises, 2 hrs pc labs, 13 hrs projects

Assessment points

60 exam, 20 half-term test, 10 labs, 10 projects

Department

Lecturer

Subject specific learning outcomes and competences

Students acquire knowledge of principles and design of intelligent systems.

Learning objectives

To acquaint students with theory and principles of intelligent systems.

Prerequisite kwnowledge and skills

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.

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

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

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

Progress assessment

  • Mid-term written test
  • PC lab
  • Individuální project

Course inclusion in study plans

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