M'AI: Agent Models 1000-1M23MAM
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.
Main fields of studies for MISMaP
Type of course
Prerequisites
Prerequisites (description)
Course coordinators
Assessment criteria
A project and an oral exam based on the project will be used for assessment.
Bibliography
* ''Active Particles'' vol. I, vol II Bellomo, Degond, Tadmor
* ''Reinforcement Learning'' Sutton, Barto
* wybrane aktualne prace naukowe
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: