Print syllabusM'AI: Agent Models
General data
| Course ID: | 1000-1M23MAM |
| Erasmus code / ISCED: |
11.1
|
| Course title: | M'AI: Agent Models |
| Name in Polish: | M’AI: Agent Models |
| Organizational unit: | Faculty of Mathematics, Informatics, and Mechanics |
| Course groups: |
Elective courses for 2nd stage studies in Mathematics |
| ECTS credit allocation (and other scores): |
6.00
|
| Language: | English |
| Main fields of studies for MISMaP: | mathematics |
| Type of course: | elective monographs |
| Prerequisites: | Ordinary differential equations I 1000-114bRRZIb |
| Prerequisites (description): | The participant should be familiar with basic concepts and methods of ordinary differential equations. |
| Short description: |
The lecture introduces participants to the theory of agent models based on ordinary differential equations and their discretization. These systems naturally arise in the theory of collective behavior, such as the formation of animal herds, schools of fish, or opinions in various groups. During the analysis, we will introduce concepts from machine learning in order to obtain desired models. The lecture will be held in cooperation with Dr. Jacek Cyranka from the Institute of Computer Science and Dr. Janek Peszek from the Institute of Applied Mathematics and Mechanics. |
| Full description: |
The lecture aims to familiarize participants with basic agent models mainly from collective dynamics. In addition to deriving models and describing their main properties, we will focus on optimizing certain parameters using numerical optimization based on machine learning techniques. Often, when given a general tendency of group behavior, we seek a local interaction rule among agents that leads to behavior consistent with the given tendency. This rule should define our model or class of models. This is an approach that is opposite to classical mechanics, where we aim to derive a model with desirable properties. Here, by definition, our system will satisfy the initial principle. A portion of the lecture will be devoted to kinetic models that naturally arise for a large number of particles. The lecture will take the form of workshops focused on the interests of participants, ranging from purely mathematical to computer science issues. The lecture will be held in cooperation with Dr. Jacek Cyranka from the Institute of Computer Science and Dr. Janek Peszek from the Institute of Applied Mathematics and Mechanics. |
| Bibliography: |
* ''Active Particles'' vol. I, vol II Bellomo, Degond, Tadmor * ''Reinforcement Learning'' Sutton, Barto * wybrane aktualne prace naukowe |
| Learning outcomes: |
(in Polish) Student wie jak rozpoznać agentów. |
| Assessment methods and assessment criteria: |
A project and an oral exam based on the project will be used for assessment. |
Classes in period "Summer semester 2023/24" (in progress)
| Time span: | 2024-02-19 - 2024-06-16 |
 
MO TU W WYK-MON
TH FR |
| Type of class: |
Monographic lecture, 30 hours
|
|
| Coordinators: | Piotr Mucha | |
| Group instructors: | Piotr Mucha | |
| Students list: | (inaccessible to you) | |
| Examination: | Examination |
Copyright by University of Warsaw.
