Basic information
- Field of study
- Computer Science and Data Science
- Major
- All
- Organisational unit
- Faculty of Computer Science
- Study level
- Second-cycle (engineer) programme
- Form of study
- Full-time studies
- Profile
- General academic
- Didactic cycle
- 2025/2026
- Course code
- WIIDSS.IIi1.02895.25
- Lecture languages
- Polish
- Mandatoriness
- Elective
- Block
- Core Modules
- Course related to scientific research
- Yes
|
Period
Semester 1
|
Method of verification of the learning outcomes
Completing the classes
Activities and hours
Lectures:
14
Laboratory classes: 28 |
Number of ECTS credits
3
|
Goals
| C1 | Providing students with knowledge of modern approaches and algorithms used in modeling and simulation, with particular emphasis on the agent-based approach. |
| C2 | To acquaint students with the consecutive stages of developing the agent-based model and simulation of a selected real-world phenomenon. |
| C3 | To acquaint students with the methods of conducting simulation experiments and developing and presenting their results. |
Course's learning outcomes
| Code | Outcomes in terms of | Learning outcomes prescribed to a field of study | Methods of verification |
| Knowledge – Student knows and understands: | |||
| W1 | basic types of models and simulation systems. | INFDS2A_W01, INFDS2A_W02, INFDS2A_W04 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
| W2 | basic simulation control mechanisms. | INFDS2A_W01, INFDS2A_W02 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
| W3 | the concepts of agent-based modeling and simulation as well as the principles of creating agent-based models of selected real-world phenomena. | INFDS2A_W01, INFDS2A_W02, INFDS2A_W04 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
| W4 | techniques and algorithms used in the implementation of agent-based simulation systems. | INFDS2A_W01, INFDS2A_W02, INFDS2A_W03 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
| Skills – Student can: | |||
| U1 | conduct simulation experiments using the implemented software and develop, interpret and describe the results of experiments. | INFDS2A_U01, INFDS2A_U03, INFDS2A_U04, INFDS2A_U05, INFDS2A_U06, INFDS2A_U07 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
| U2 | develop an agent-based model of the chosen phenomenon. | INFDS2A_U02, INFDS2A_U04, INFDS2A_U05, INFDS2A_U06, INFDS2A_U07 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
| U3 | implement a developed agent-based simulation model using selected tools and programming libraries. | INFDS2A_U01, INFDS2A_U02, INFDS2A_U03, INFDS2A_U04, INFDS2A_U06, INFDS2A_U07 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
| Social competences – Student is ready to: | |||
| K1 | analyse and critically evaluate non-technical aspects of the application of modeling and simulation techniques. | INFDS2A_K01, INFDS2A_K02 | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research |
Program content ensuring the achievement of the learning outcomes prescribed to the module
As part of the course, the student acquaints with the basic mechanisms, techniques, and approaches to modeling and simulation with particular emphasis on the agent-based approach.
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 14 | |
| Laboratory classes | 28 | |
| Realization of independently performed tasks | 14 | |
| Preparation of project, presentation, essay, report | 28 | |
| Contact hours | 5 | |
| Student workload |
Hours
89
|
|
| Workload involving teacher |
Hours
42
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
1. Basic concepts and issues: system, system versus environment, model, agent, object, attributes, system state, event, action, process. Discrete systems. Continuous systems. Simulation models. Experiments with an existing system or system model. The physical model and the mathematical model. Analytical solution and simulation. (2 hours) |
W1, W2, W3, W4, K1 | Lectures |
| 2. |
1. Development of an agent-based model of a selected biological, social, economic or other phenomenon (4 hours). |
W1, W2, W3, W4, U1, U2, U3, K1 | Laboratory classes |
Extended information/Additional elements
Teaching methods and techniques :
Lectures will be conducted remotely using the MS Teams platform. Other classes will be held in classrooms., Discussion, Group work, E-learning, Project Based Learning, Mentoring, Tutoring, Inquiry Based Learning, Lecture
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research | Completing a project on a topic agreed with the teacher. |
| Lab. classes | Activity during classes, Project, Report, Presentation, Completion of laboratory classes, Preparation and conduct of scientific research | Completing a project on a topic agreed with the teacher. |
Additional info
Lectures on the subject will be conducted remotely using the MS Teams platform.
Other classes will be held in classrooms. This also applies to credits and exams taking place during examination sessions.
Conditions and the manner of completing each form of classes, including the rules of making retakes, as well as the conditions for admission to the exam
Lectures: completing a project on a topic agreed with the teacher.
Laboratory classes: completing a project on a topic agreed with the teacher.
Re-takes: completing a project on a topic agreed with the teacher.
Method of determining the final grade
The final grade is identical to the grade from the laboratory classes: 0.25 * development of the simulation model + 0.25 * implementation of the simulation model + 0.25 * conducting simulation experiments + 0.25 * development, interpretation, and presentation of results.
Manner and mode of making up for the backlog caused by a student justified absence from classes
Completing a project on a topic agreed with the teacher.
Prerequisites and additional requirements
Ability to program in Java/Scala/Python/C++ or another language in which it is possible to implement a selected simulation system.
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Lectures: Students participate in classes learning the teaching content in accordance with the subjects from the syllabus. Students should keep asking questions and clarifying doubts. Audiovisual registration of the lecture requires the teacher's consent.
Laboratory classes: Students carry out practical work aimed at obtaining competences assumed by the syllabus. The method of project implementation and the final result are assessed.
Literature
Obligatory- Grimm V., Railsback S.F., _Agent-Based and Individual-Based Modeling: A Practical Introduction_, Princeton University Press, 2011.
- Epstein J.M., _Generative Social Science: Studies in Agent-Based Computational Modeling_, Princeton University Press, 2007
- Uhrmacher A.M., Weyns, D. (red.), _Multi-Agent Systems. Simulation and Applications_, CRC Press, 2009
- Gilbert N., _Agent-based models_, SAGE Publications, 2008.
- Hamill L., Gilbert N., _Agent-Based Modelling in Economics_, Wiley, 2016.
- North M.J., Macal, C.M., _Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation_, Oxford University Press, 2007.
- Wilensky U., Rand, W., _An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo_, The MIT Press, 2015.
- Lee R.S.T. (red.), _Computational Intelligence for Agent-based Systems_, Springer-Verlag, 2007.
- Dorigo M., Stützle, T., _Ant Colony Optimization_, The MIT Press, 2004.
- Engelbrecht A.P., _Fundamentals of Computational Swarm Intelligence_, Wiley, 2005.
- Floreano D., Mattiussi C., _Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies_, The MIT Press, 2008.
- Sarker R.A., Ray T., (red.), _Agent-Based Evolutionary Search_, Springer, 2010.
- Grimm V., Railsback S.F., _Individual-based Modeling and Ecology_, Princeton University Press, 2005.
- Sterling L.S., _The Art of Agent-Oriented Modeling_, The MIT Press, 2009.
- Russell S., Norvig P., _Artificial Intelligence: A Modern Approach_, Pearson, 2010.
- Ferber J., _Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence_, Addison-Wesley, 1999.
- Wooldridge M., _An Introduction to MultiAgent Systems_, Wiley, 2009.
- Gilbert N., Troitzsch K.G., _Simulation for the Social Scientist_, Open University Press, 2005
- Epstein J.M., Axtell, R., _Growing artificial societies. Social science from bottom up_, Brookings Institution Press, The MIT Press, 1996.
- Banks J., Carson J, Nelson B.L., Nicol D., _Discrete-Event System Simulation_, Prentice Hall, 2004
- Zeigler B. P., Kim, T. G., Praehofer H., _Theory of Modeling and Simulation_, Academic Press, 2000
- Severance, F. L., _System Modeling and Simulation: An Introduction_, Wiley, 2001
Scientific research and publications
Research- Agentowe modelowanie i symulacja zjawisk biologicznych, społecznych i ekonomicznych.
- R. Dreżewski. The agent-based model and simulation of sexual selection and pair formation mechanisms. Entropy, 20(5):342, 2018.
- R. Dreżewski. Agent-based modeling and simulation of speciation and ecosystem diversity. In Andri Pranolo, Adhi Prahara, Ahmad Azhari, and Agus Aktawan, editors, 2018 International Symposium on Advanced Intelligent Informatics (SAIN). Revolutionize Intelligent Informatics Spectrum for Humanity, August 29–30, 2018, Yogyakarta, Indonesia, pages 210–215. IEEE, 2019.
- R. Dreżewski. Agent-based simulation model of sexual selection mechanism. In G. Jezic, R. J. Howlett, and L. C. Jain, editors, Agent and Multi-Agent Systems: Technologies and Applications. 9th KES International Conference, KES-AMSTA 2015 Sorrento, Italy, June 2015, Proceedings, volume 38 of Smart Innovation, Systems and Technologies, pages 155-166. Springer International Publishing, 2015.
- A. Byrski, R. Dreżewski, L. Siwik, and M. Kisiel-Dorohinicki. Evolutionary multi-agent systems. The Knowledge Engineering Review, 30(2):171-186, 2015.
- R. Dreżewski. Agent-based modeling and simulation of species formation processes. In F. Alkhateeb, E. Al Maghayreh, and I. Abu Doush, editors, Multi-Agent Systems – Modeling, Interactions, Simulations and Case Studies, pages 3-28. InTech, Rijeka, 2011.
- R. Dębski and R. Dreżewski. Adaptive surrogate-assisted optimal sailboat path search using onboard computers. In Derek Groen, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, and Peter M. A. Sloot, editors, Computational Science — ICCS 2022, pages 355–368, Cham, 2022. Springer International Publishing.
- R. Dębski and R. Dreżewski. Surrogate-Assisted Ship Route Optimisation. In Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M., editors, Computational Science – ICCS 2023, pages 395–409, Cham, 2023. Springer International Publishing.
- R. Dębski and R. Dreżewski. Multi-objective ship route optimisation using estimation of distribution algorithm. Applied Sciences, 14(13), 2024.