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Multi-Agent Systems Designing Methods
Major: Computer Sciences
Code of subject: 7.122.03.E.029
Credits: 5.00
Department: Computer-Aided Design
Lecturer: Nazarii B. Yavorskyi, Ph.D., Associate professor of the CAD Department
Semester: 2 семестр
Mode of study: денна
Завдання: .
Learning outcomes: .
Required prior and related subjects: - prerequisites:
• Intelligent Data Analysis,
• Methods and Systems of Artificial Intelligence;
- co-requisites:
• Design Automation of Intelligent Embedded Systems,
• Methods of Fuzzy Logic and Evolutionary Algorithms for the Automated Design,
• Decision Support Systems in Automated Design.
Summary of the subject: .
Опис: - methods of designing multiagent systems;
- types of agents in multiagent systems;
- the interaction of agents in multiagent systems;
- the basis of mutual understanding and communication of agents in multiagent systems;
- arrangements between agents in multiagent systems.
Assessment methods and criteria: lectures, laboratory sessions, self-study.
Критерії оцінювання результатів навчання: - Current control (45%): written reports on laboratory work, settlement and graphic work, independent work, oral examination;
- Final control (55% of exam): in written, verbally.
Recommended books: 1. Subbotin S., Oleynik A. Modifications of Ant Colony Optimization Method for Feature Selection // The experience of designing and application of CAD systems in Microelectronics: Proceedings of the IX International Conference CADSM – 2007 (20–24 February 2007). – Lviv: Publishing house of Lviv Polytechnic, 2007. – P. 493–494.
2. Джонс М.Т. Программирование искусственного интеллекта в приложениях. – М.: ДМК Пресс, 2004. – 312 с.
3. Дубровин В.И., Субботин С.А., Богуслаев А.В., Яценко В.К. Интеллектуальные средства диагностики и прогнозирования надежности авиадвигателей: Монография. – Запорожье: ОАО «Мотор – Сич», 2003. – 279 с.
4. Дубровін В.І., Субботін С.О. Методи оптимізації та їх застосування в задачах навчання нейронних мереж: Навч. Пос. – Запоріжжя: ЗНТУ, 2003. – 136 с.
5. Люгер Дж.Ф. Искусственный интеллект: стратегии и методы решения сложных проблем / Пер. с англ.. – М.: Вильямс, 2005. – 864 с.
6. Олейник Ал.А. Сравнительный анализ методов оптимизации на основе метода муравьиных колоний // Комп’ютерне моделювання та інтелектуальні системи: Збірник наукових праць / За ред. Д.М. Пізи, С.О. Субботіна. – Запоріжжя: ЗНТУ, 2007. – С. 147–159.
7. Рассел С., Норвиг П. Искусственный интеллект: современный подход, 2 – е изд.: Пер с англ. – М.: Вильямс. – 2006. – 1408 c.
1. Bullnheimer B., Hartl R.F., Strauss C. Applying the ant system to the vehicle routing problem // Meta – Heuristics: Advances and Trends in Local Search Paradigms for Optimization. – Boston: Kluwer, 1998. – P. 109–120.
2. Costa D., Hertz A. Ants can colour graphs // Journal of the Operational Research Society. – 1997. – №48. – P. 295–305.
3. Dorigo M. Optimization, Learning and Natural Algorithms. – Milano: Politecnico di Milano, 1992. – 140 p.
4. Dorigo M., Gambardella L.M. Ant colonies for the traveling salesman problem // BioSystems. – 1997. – №43. – P. 73–81.
5. Gambardella L.M., Dorigo M. HAS – SOP: An hybrid ant system for the sequential ordering problem. – Lugano: CH, 1997. – P. 237–255.
6. Gambardella L.M., Taillard E., Agazzi G. Macs – vrptw: A multiple ant colony system for vehicle routing problems with time windows // New Methods in Optimisation. – McGraw – Hill, 1999. – P. 63–79.
7. Maniezzo V. Exact and approximate nondeterministic tree – search procedures for the quadratic assignment problem. – Bologna: Universita di Bologna, 1998 – 102 p.
8. Michel R., Middendorf M. An ACO algorithm for the shortest common supersequence problem // New Methods in Optimisation. – McGraw – Hill, 1999. – P. 525–537.