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Algorithms for learning optimal paths are present in several scenarios. Given this, LRTA* (learning real time A*) appears as an option for reconciling planning and action. This article investigate how the variation of the number of agents impacts on the distances traveled by them to find optimal path using the LRTA* in static environments. Experiments show the existence of a relationship that the grater number of agents, the total quantity and per capita movements behave like mathematical curves, the first one like a linear and the second as a decreasing exponential. Through the relationship, it is possible to define the best number of agents in the search for the optimal path in terms of performance.

BRACIS 2021

Impact of the variation of the number of agents in the cooperative learning of optimal paths using LRTA*

lrta*

cooperative learning

multi-agent systems

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.

BRACIS 2020

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

[Register now](http://c4ai.inova.usp.br/bracis2021/)

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Organized by C4AI, the 10th Brazilian Conference on Intelligent Systems (BRACIS) will take place from November 29th to December 3rd, 2021.

Species Distribution Modelling (SDM) is used for biodiversity preservation and wildlife management. In order to apply it, a large and reliable
dataset about the species occurrence is required. However, in some cases, this
can be difficult when there are only a few occurrence records. In this context,
uncertainty handling techniques can be applied. Thus, Bayesian inference was
used in this study to perform SDM in the Amazon Basin region near Manaus
(AM) with data collected by the GoAmazon 2014/15 project. The results were
compared with those obtained in statistical models and were similar.

Handling uncertainty through Bayesian inference for Species Distribution Modelling in the Amazon Basin region

Computational applications based on sensor data is a reality, but the
data collected and transmitted to the applications rarely arrives ready for use
due to losses and noise of various types. In this work, an approach based on spatial temporal correlation is developed for cleaning data from multiple temporal
series of sensors for noise, missing data and outliers. The method was tested
on six publicly available real datasets and its performance was compared with
a baseline method, an autoencoder denoising, and another published method.
The results show that the proposed approach is competitive and requires less
training data than competitors.

Data Cleansing of Multiple Environmental Monitoring Time Series Using Spatio-Temporal Correlation

Time series have great applicability in the most diverse scenarios, including the scientific, agricultural, economic domains, among others.
Therefore, creating effective representations of a time series is a challenging task, as it allows more accurate analysis and, consequently, more assertive results and conclusions in various machine learning tasks.
One of the main tasks is classification, which can be performed from different computational representations of time series.
The main objective of this work is to improve the efficiency of classification tasks using a representation of time series obtained by the Beam Angle Statistics algorithm, a contour feature extractor based on angular statistics.

Time Series Classification using Shape Features based on Angle Statistics

The use of Artificial Intelligence and Machine Learning algorithms in everyday life is common nowadays in several areas, bringing many possibilities and benefits to society. However, since there is room for learning algorithms to make decisions, the range of related ethical issues was also expanded. There are many complaints about Machine Learning applications that identify some kind of bias, disadvantaging or favoring some group, with the possibility of causing harm to a real person. The present work aims to shed light on the existence of biases, analyzing and comparing the behavior of different learning algorithms – namely Decision Tree, MLP, Naive Bayes, Random Forest, Logistic Regression and SVM – when being trained using biased data. We employed pre-processing algorithms for mitigating bias provided by IBM’s framework AI Fairness 360.

Performance analysis of machine learning algorithms trained on biased data

Chess is becoming more popular and accessible by the day. For instance, online chess enables matches between players from different parts of the world, bringing new ways of learning the game and interacting with other Web users. With this growth in popularity, there is a possibility to empower amateur players with rich computer analysis and tools, which may assist them in their learning process. One of the ways to analyze chess matches is through the study of errors. In this context, we present a new approach for the task of error prediction in chess. Our motivation is that knowing when players are likely to make a mistake is knowing what types of situations lead to difficulties in making the right decision. To that end, we add an abstraction layer to the already studied error prediction task, providing graph-based features to the machine learning models. Our results show an increase in the accuracy of the tested models, improving the results obtained in recent studies.

A lightweight approach for predicting errors in chess matches

Policy Search (PS) methods have been used to learn optimization algorithms, obtaining encouraging results. In this work, we used PS methods to learn optimization algorithms for global optimization problems, considering a little-studied scenario: high-dimensional functions and the optimization algorithms do not have access to the derivatives of the function to be optimized. The results indicate that, despite the difficulties, the learned optimization algorithms have promising performance in the studied scenario.

Zeroth Order Policy Search Methods for Global Optimization Problems: An Experimental Study

There is a growing interest in systems that detect violence automati-
cally using ambient audio. In this work, we built and evaluated four classifiers
with this proposal. However, instead of directly processing the audio signals, we
converted them to images, known as Mel-spectrograms, and then used Convolu-
tional Neural Networks (CNN) to treat as an image classification problem using
pre-trained networks in this context. We tested the architectures Inception v3,
VGG-16, MobileNet v2, and ResNet152 v2, and the classifier coming from the
MobileNet architecture obtained the best classification results when evaluated
on the HEAR Dataset created to carry out this research.

Deep Learning and Mel-spectrograms for Physical Violence Detection in Audio

We propose a solution based on Multi-Layer Perceptron (MLP) to predict the offset of the center of gravity of an offshore platform. We also perform a comparative study with three optimization algorithms – Random Search, Simulated Annealing, and Bayesian Optimization (BO) – to find the best MLP architecture. Although BO obtained the best architecture in the shortest time, ablation studies developed with hyperparameters of the optimization process showed that the result is sensitive to them and deserves attention in the Neural Architecture Search process.

Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction

Training deep neural networks is a relevant problem with open questions related to convergence and quality of learned representations. Gradient-based optimization methods are used in practice, but cases of failure and success are still to be investigated. In this context, we set out to better understand the convergence properties of different optimization strategies, under different parameter options. Our results show that (i) feature embeddings are impacted by different optimization settings, (ii) suboptimal results are achieved by the use of default parameters, (iii) significant improvement is obtained by making educated choices of parameters, (iv) learning rate decay should always be considered. Such findings offer guidelines for training and deployment of deep networks.

Optimization Matters: Guidelines to Improve Representation Learning with Deep Networks

Automatic gender recognition from speech is a problem related to the
area of speech analysis and has a variety of applications thats extends from the
personalisation of product recommendation to forensics. Identifying the efficiency and costs of different approaches that deal with this problem is imperative. This work aims to investigate and compare the efficiency and costs of different deep learning architectures in the task of gender recognition from speech.

Impact of the variation of the number of agents in the cooperative learning of optimal paths using LRTA*

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Handling uncertainty through Bayesian inference for Species Distribution Modelling in the Amazon Basin region

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