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Интеллектуальные агенты и мультиагентные системы (ИЯ)
Интеллектуальные агенты и мультиагентные системы (ИЯ)
The course intends to explore the concept of Agents and Multi-Agents as well as introduce students to approaches required to model such agents in a real-world scenario.
Learning Outcomes:
At the end of the course, the student will be able to:
Understand the notion of an agent/multi-agent and understand the characteristics of applications that lend themselves to an agent/multi-agent-oriented solution;
Understand the key issues associated with constructing agents capable of intelligent autonomous action, and the main approaches taken to developing such agents;
Understand the key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systems
Understand the main application areas of agent-based solutions, and be able to develop a meaningful agent-based system using a contemporary agent development platform.
Source: Geeks4Geeks
Course Outline
Course Outline
Socio-Technical Systems
Complex Systems (Social Networks, Heterogeneity, Emergence, Feedback, Leverage Points, Path Dependence)
Knowledge Representation (KR), Reasoning, 1st Order Logic & Formal Logic
Introduction to Agents and Multi-Agent Systems (MAS)
Introduction to Game Theory
Introduction to Agent-Based Modelling (ABM)
Real-World Applications (Modelling Socio-Economic Activities)
Tools: NetLogo
Applications and Case Studies
Designing, Modelling and Evaluating Agents
Artificial Intelligence
Self-Organizing Agents
Reinforcement Learning (+ a brief Introduction to Markov Decision Processes)
Grading:
Attendance - 10 %
Assignments - 20 %
Project - 20 %
Exam - 50 %
Notification: Make sure you notify me 24 hours before you miss a class.
Assignments:
All Essay Assignments MUST Be Cited (APA Style)!
Plagiarism will be checked and penalized accordingly.
Make sure you submit All Assignments before the stipulated deadline.
End of Semester Tasks:
Model Design and Building
Weekly Rapidfire Presentation Sessions
1 page Review on a Selected Set of Papers
End of Semester Project
End of Semester Project
After approximately 3 weeks of the course, students must select a topic that will be their focus during the semester and perform one of the following:
A Research Paper for Publication in a scientific journal/conference must be prepared. The paper can cover any of the following methodological scopes under the course content- Model Building Experimental, Simulation, Comparative Study, Review of State of the Art.
A Code-Based Project (based on a case study) that takes into consideration the concepts studied throughout the course
Caveat: Maximum of 2 students in a group or Individual work.
Note: This is task contributes to the final exam score and is mandatory to pass the course.
The final project will be presented on the final day of exam task submission.
Bibliography
Bibliography
Van der Hoek, W., & Wooldridge, M. (2008). Multi-agent systems. Foundations of Artificial Intelligence, 3, 887-928.
Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.
Abdou, M., Hamill, L., & Gilbert, N. (2012). Designing and building an agent-based model. In Agent-based models of geographical systems (pp. 141-165). Springer, Dordrecht.
Boero, R., Morini, M., Sonnessa, M., & Terna, P. (2015). Agent-based models of the economy: from theories to applications. Springer.
Billari, F. C., Fent, T., Prskawetz, A., & Scheffran, J. (Eds.). (2006). Agent-based computational modelling: applications in demography, social, economic and environmental sciences. Taylor & Francis.
Gallegati, M. (2018). Complex Agent-Based Models. Berlin: Springer.
Nakai, Y., Koyama, Y., & Terano, T. (2013). Agent-Based Approaches in Economic and Social Complex Systems VIII. Springer, New York.
Somarathna, K. U. S. (2020). An agent-based approach for modeling and simulation of Human Resource Management as a complex system: Management strategy evaluation. Simulation Modelling Practice and Theory, 102118.
Held, F. P., Wilkinson, I. F., Marks, R. E., & Young, L. (2014). Agent-based Modelling, a new kind of research. Australasian Marketing Journal (AMJ), 22(1), 4-14.
Auchincloss, A. H., & Garcia, L. M. T. (2015). Brief introductory guide to agent-based modeling and an illustration from urban health research. Cadernos de saude publica, 31, 65-78.
Sabzian, H., Shafia, M. A., Maleki, A., Hashemi, S. M. S., Baghaei, A., & Gharib, H. (2019). Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners. arXiv preprint arXiv:1901.08932.
Dorri, A., Kanhere, S. S., & Jurdak, R. (2018). Multi-agent systems: A survey. Ieee Access, 6, 28573-28593.
Aleta, A., Martín-Corral, D., y Piontti, A. P., Ajelli, M., Litvinova, M., Chinazzi, M., ... & Pentland, A. (2020). Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour, 1-8.
Luck, M., Marik, V., Stepankova, O., & Trappl, R. (Eds.). (2001). Multi-Agent Systems and Applications: 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School, EASSS 2001, Prague, Czech Republic, July 2-13, 2001. Selected Tutorial Papers (Vol. 2086). Springer Science & Business Media.
Moore, G. H. (1988). The emergence of first-order logic. History and philosophy of modern mathematics, 11, 95-135.
Sridharan, M., & Gelfond, M. (2016, July). Using Knowledge Representation and Reasoning Tools in the Design of Robots. In KnowProS@ IJCAI.
Rajangam, E., & Annamalai, C. (2016). Graph models for knowledge representation and reasoning for contemporary and emerging needs–a survey. International Journal of Information Technology and Computer Science (IJITCS), 8(2), 14-22.
Croitoru, M., Marquis, P., Rudolph, S., & Stapleton, G. (Eds.). (2018). Graph structures for knowledge representation and reasoning. Springer International Publishing.
Ayorinde, I., & Akinkunmi, B. (2013). Application of first-order logic in knowledge based systems. Afr J. of Comp & ICTs, 6, 45-52.
Miller, John H., and Scott E. Page. Complex adaptive systems: An introduction to computational models of social life. Vol. 17. Princeton university press, 2009.
Goertzel, B., Giacomelli, S., Hanson, D., Pennachin, C., & Argentieri, M. (2017). SingularityNET: A decentralized, open market and inter-network for AIs. Thoughts, Theories Stud. Artif. Intell. Res.
Epstein, Joshua M. "Why model?." Journal of artificial societies and social simulation 11.4 (2008): 12.
online lecture platform and tools (remember to download Microsoft Teams & use your student login to access resources)
Tools
Tools
The practical aspect of the course will require implementing and running agent-based models using the following tools:
Lab Sessions - NetLogo
Lab Sessions - NetLogo
- Tutorial 1 (Models) - http://ccl.northwestern.edu/netlogo/docs/tutorial1.html (Wolf Sheep Predation)
- Tutorial 2 (Commands) - http://ccl.northwestern.edu/netlogo/docs/tutorial2.html (Basic Traffic Model )
- Tutorial 3 (Procedures) - http://ccl.northwestern.edu/netlogo/docs/tutorial3.html
- Tutorial 4 - http://www.cs.mun.ca/~harold/Courses/Old/CS2000.W12/Labs/CS2000_Lab_Robotics_Wareham.pdf
Complementary Resources and Reading Materials
Complementary Resources and Reading Materials
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