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Lecturer(s)
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Diviš Roman, Ing. Ph.D.
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Bažant Michael, doc. Ing. Ph.D.
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Course content
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1. On-line simulation and its comparison with off-line simulation, concepts of training simulators 2. Applications of simulation techniques for supporting different kinds of planning (reactive versus pro-active approach) 3. Paradigm of autonomous agents - agent-based architectures of simulating systems (ABAsim architecture) 4. Agent decomposition into internal components, agent-based simulators versus event-driven and process-driven simulators 5. Complex case study - agent-based traffic simulator reflecting the operation within transportation nodes 6. Flexible message-passing paradigm within hierarchical agent-based systems (address messages, partially addressed messages, non-addressed messages) 7. Synchronizations of discrete -continuous simulations within the frame of agent-based architectures 8. Methods of artificial intelligence and soft computing supporting making decisions within simulators 9. Petri nets - definition, transition enabling/firing rules, reachability graph, evolution steps 10. Petri nets - formal description of technological processes and simulator's components, application examples 11. Concept of distributed and parallel simulations, agent-based distributed simulators 12. Distributed simulations - synchronisations applying conservative methods 13. Distributed simulations - optimistic synchronising methods
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Skills training
- Contact teaching
- 52 hours per semester
- Term paper
- 45 hours per semester
- Home preparation for classes
- 20 hours per semester
- Preparation for an exam
- 33 hours per semester
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Learning outcomes
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The main goal of the course is to familiarize students with advanced techniques and approaches of discrete simulation on digital computers as well as with exploitations of those experimental methods within the frame of different application domains.
Passing the course enables to manage advanced simulation techniques and design & construction of simulation models.
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Prerequisites
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There is expected elementary knowledge from the fields of discrete simulation, mathematical statistics and theory of probability.
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Assessment methods and criteria
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Oral examination, Written examination, Home assignment evaluation
Given assignment approves that a student attended lessons in a required scale and fulfilled qualified requirements (elaboration of simulation models focused on training of specific simulation techniques).
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Recommended literature
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Banks, J. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. New York: John Wiley & Sons, 1998. ISBN 0-471-13403-9.
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Jensen, K. Coloured Petri Nets: modelling and validation of concurrent systems. 2009. ISBN 978-3-642-00283-0.
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Kavička, Antonín. Agentovo orientovaná simulácia dopravných uzlov. Žilina: EDIS - vydavatel'stvo ŽU, 2005. ISBN 80-8070-477-5.
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