Courses

Course 1

GAMA Platform: Basic Concepts for Multi-Agent Modeling and Simulation

Profa. Dra. Míriam Blank Born
mbborn@inf.ufpel.edu.br
http://lattes.cnpq.br/6006565359245381

Prof. Dr. Giovani Parente Farias
giovani.pfarias@inf.ufpel.edu.br
http://lattes.cnpq.br/1018507855402721

Centro de Desenvolvimento Tecnológico
Universidade Federal de Pelotas - UFPel

Abstract: GAMA (GIS Agent-based Modeling Architecture) is a simulation platform with a complete modeling and simulation integrated development environment (IDE) for building spatially explicit agent-based simulations. GAMA was developed with a general approach and can be used for many application domains. GAMA is present mainly in applications domains like transport, urban planning, disaster response, epidemiology, analysis of multi-robot systems, and the environment, with special emphasis on GIS data analyses. GAML (GAma Modeling Language) is the dedicated language used in GAMA. It is an agent-based language that provides the possibility to build a model with several paradigms of modeling. This workshop aims to introduce basic concepts for understanding and using the GAMA platform and develop an example of multi-agent modeling and simulation using geo-referenced data.

Course 2

Multi-Agent Oriented Programming with JaCaMo with an organization-focused approach

Prof. Dr. Maiquel de Brito
maiquel.b@ufsc.br
http://lattes.cnpq.br/7806025869273339

Departamento de Engenharia de Controle, Automação e Computação
Universidade Federal de Santa Catarina - UFSC Blumenau

GITHUB

Abstract: Multi-Agent Oriented Programming (MAOP) is an approach to developing Multi-Agent Systems (MAS) considering four complementary dimensions: agents, interaction, environment, and organization. Agents are autonomous agents acting in the system and possibly interacting with one another to accomplish the system's objectives. The environment contains non-autonomous entities that provide resources for the agents to exploit to achieve their goals. The organization coordinates the agents' activities to achieve the system's overall goals. This course introduces MAOP using the JaCaMo framework, which provides resources to develop the four dimensions of MAS and integrate them. Essential concepts on agents, interaction, and environment are introduced. Then, the organization is presented with a slightly deeper focus for the audience to understand the coordination of agents interacting in the environment. The theoretical background on all the concepts is complemented with hands-on activities to develop practical skills on MAOP.

Course 3

Introduction to Distributed and Embedded Multi-agent Systems

Prof. MSc. Nilson Mori Lazarin
nlazarin@cefet-rj.br
http://lattes.cnpq.br/4304954704049203

Prof. Dr. Carlos Eduardo Pantoja
pantoja@cefet-rj.br
http://lattes.cnpq.br/4343654212657424

Centro Federal de Educação Tecnológica Celso Suckow da Fonseca
UnED Nova Friburgo

Abstract: Several application domains have used the Internet of Things (IoT) paradigm by using devices that perceive the real-world environment and produce raw data to be processed in the cloud. On the other hand, pervasive computing has used Artificial Intelligence (AI) paradigm to promote information extraction and decision-making at the edge computing layer. Thus, the intersection between the paradigms of AI and IoT conceived a new area of possibilities called Artificial Intelligence of Things (AIoT). Considering this scenario, we introduce the development of cognitive, autonomous, and collaborative IoT devices that are capable of perceiving, deciding, and acting in the real world, using an embedded multi-agent systems approach that allows the implementation of mental attitudes of beliefs, desires, and intentions into physical devices. In this workshop, students will understand how to use a specialized version of the Jason framework for embedded systems; they will use an Integrated Development Environment to program the Firmware and Reasoning layers of cognitive devices; they will know about a specific-purpose Operating System for embedded multi-agent systems; and finally, they will access an IoT network to allow communication between autonomous agents and cognitive hardware.

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