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
Мета вивчення дисципліни: • to gain knowledge and practical skills in using multiagent systems and intelligent agents for solving practical problems; • to have an idea about the current state and future development of agent technologies and software for designing and developing agent systems.
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.