The development of new communication networks to offer
innovative services has increased the volume of data. With the introduction of
Deep Reinforcement Learning and Service Function Chaining architecture,
new research opportunities have emerged to propose solutions to the new
challenges. This work proposes a model to demonstrate through
computational simulations how these techniques can be applied. The model
was evaluated using two variations of the Deep Q-Network algorithm over the
CIC-Darknet dataset. Results showed that both variations are a promising
mechanism to make the networks more autonomous and intelligent.

A Model for Traffic Forwarding through Service Function Chaining using Deep Reinforcement Learning Techniques

This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

Stock market forecasting has been a quite popular challenge in machine learning research. Recently, studies have been using deep learning techniques, such as Convolutional Neural Networks (CNN), to perform regressions in prices or classification in the trading signal as an operation indication. 
However, these works have not yet achieved a satisfactory financial result. In this paper, we propose a financially profitable stock market method that uses a new approach called Multichannel CNN Trading Classifier (MCNN-TC).
The model was evaluated using data from the Brazilian stock market. The results indicate a promising financial trading performance compared to the Buy and Hold strategy and good classification metrics.

Stock Trading Classifier with Multichannel Convolutional Neural Network

Trading in the stock market always comes with the challenge of deciding
the best action to take on each time step. The problem is intensified by the
theory that it is not possible to predict stock market time series as all information
related to the stock price is already contained in it. In this work we propose a
novel model called Discrete Wavelet Transform Gated Recurrent Unit Network
(DWT-GRU). The model learns from the data to choose between buying, holding
and selling, and when to execute them. The proposed model was compared to
other recurrent neural networks, with and without wavelets preprocessing, and
the buy and hold strategy. The results shown that the DWT-GRU outperformed
all the set baselines in the analysed stocks of the Brazilian stock market.

Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market

**Chairs:** Marlo Vieira Dos Santos e Souza and Ricardo Marcacini


Runners Up Best Paper (Main Track)

technical paper

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

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BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

ENIAC: Technical Session 3

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

Reinaldo Bianchi

4

6

Presentations

A Model for Traffic Forwarding through Service Function Chaining using Deep Reinforcement Learning Techniques

Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

Stock Trading Classifier with Multichannel Convolutional Neural Network

Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market

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