Course details

Intelligent Systems

SIN Acad. year 2007/2008 Winter semester 5 credits

Current academic year

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

Janoušek Vladimír, doc. Ing., Ph.D. (DITS)

Language of instruction

Czech

Completion

Examination

Time span

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

Department

Department of Intelligent Systems (UITS)

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 knowledge 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. Zeigler, B.P.: Theory of Modeling and Simulation, Academic Press; 2 edition (March 15, 2000), ISBN 978-0127784557
    3. Valeš, M.: Inteligentní dům. Brno, Vydavatelství ERA, 2006.
    4. Přibyl, P., Svítek, M.: Inteligentní dopravní systémy, Nakladatelství BEN, Praha 2001, ISBN 80-7300-029-6
    5. Automatizace. http://www.automatizace.cz/

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 of numerical exercises

  1. Jazyky pro umělou inteligenci
  2. Základy Smalltalku
  3. Modelování na bázi DEVS
  4. Modelování inteligentních systémů
  5. Simulovaná robotika

Progress assessment

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Controlled instruction

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