Agent Based Modeling and Simuation (with Learning)

Welcome to the course of Agent Based Modeling and Simulation (ABMS) and Learning. The course intends to explore the use of learning techniques in ABMS. For this, traditional ABMS, as developed using NetLogo, is introduced; as well as the BDI approach to social simulation. Then, Reinforcement Learning is reviewed in detail. This self learning approach is complemented with the study of Intentional Learning for BDI agents, i.e., learning when to adopt an intention from experience. After that, the interaction between Data Minining and ABMS is explored. The organization of the course is as follows:

  1. Introduction | abms-syllabus
  2. Agent Based Modeling and Simulation | 10; 8:1 | abms-slides-01
  3. Getting Started with NetLogo | 9; 8:2 | abms-slides-02
  4. The ODD Protocol | 8:3 | abms-slides-03
  5. Implementing a First Agent Based Model | 8:4 | abms-slides-04
  6. From Animation to Science | 8:5 | abms-slides-05
  7. Collectives | 8:16 | abms-slides-06
  8. Analyzing and Understanding ABMs | 8:22 | abms-slides-07
  9. Introduction to Reinforcement Learning | 9:1 | abms-slides-08
  10. Finite Markov Decision Processes  | 9:3 | abms-slides-09
  11. Temporal-Difference Learning | 9:6 | abms-slides-10

X:Y stands for reference X, chapter Y.

Ligas

  1. Guía de estilo en NetLogo | URL

References

  1. C. Adam and B. Gaudou. BDI agents in social simulations: a survey. The Knowledge Engineering Review, 31(3):207–238, 2016.
  2. R. H. Bordini, J. F. Hübner, and M. Wooldridge. Programming Multi-Agent Systems in Agent-Speak using Jason. John Wiley & Sons Ltd, 2007.
  3. F. Grimaldo, M. Lozano, F. Barber, and A. Guerra-Hernández. Towards a model for urban mobility social simulation. Progress in Artificial Intelligence, 1(2):1–8, 2012. 10.1007/s13748-012-0012-z.
  4. A. Guerra-Hernández, R. Mondragón-Becerra, and N. Cruz-Ramírez. Explorations of the BDI multi-agent support for the knowledge discovery in databases process. Research in Computing Science, 39:221–238, 2008.
  5. A. Guerra-Hernández, C. A. González-Alarcón, and A. E. F. Seghrouchni. Jason induction of logical decision trees. In G. Sidorov and A. Hernández, editors, MICAI 2010, Part I, volume 6437 of Lecture Notes in Artificial Intelligence, pages 374–385, Berlin Heidelberg, 2010. Springer-Verlag.
  6. X. Limón, A. Guerra-Hernández, N. Cruz-Ramírez, and F. Grimaldo. Modeling and implementing distributed data mining strategies in JaCaDDM. Knowledge and Information Systems, 60(1):99-143, 2019.
  7. C. M. Macal and M. J. North. Tutorial on agent-based modelling and simulation. Journal of simulation, 4(3):151–162, 2010.
  8. S. F. Railsback and V. Grimm. Agent-Based and Individual-Based Modeling. Princeton University Press, 41 William Street, Princeton, New Jersey, USA, 2012.
  9. R. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA., USA, 1998.
  10. U. Wilensky and W. Rand. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press, Cambridge, MA., USA, 2015.
Publicado en Páginas