Brazil

The test phase is a crucial step to seek the correction of the entire software system to provide guarantees of operation and safety for users. However, applications based on multiagent systems have greater difficulty of being tested due to their properties such as autonomy, reactivity, pro-activity, and social skills. An organizational model imposes some restrictions on the behavior of agents since they are a set of behavioral constraints. This paper presents a method for systematic testing of multiagent systems specified under the Moise+ organizational model. First, a mapping is performed on a colored Petri net to measure the number of test cases needed to verify the system using adequacy criteria called the state transition path. Then each test case is specified from the Petri Net model in detail to be used as a guide in a test environment. The results indicate that the methodology can measure the testability and generate use cases for a Moise+ specification, guaranteeing a correction degree for a social level of a multiagent system.

BRACIS 2020

Testing Multiagent Systems under Organizational Model Moise using a Test Adequacy Criterion based on State Transition Path

moise

testability

petri nets

organization

**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**.

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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

technical paper

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.

Agent-based applications need to be able to explain their reasoning path that led them to adopt a given behavior. An integral part of practical intelligent agents is the goal selection, where the aim is to chose the goals that agents will commit to, so it is important to endow the agents with the ability to explain their decisions about the goals they have pursued or are pursuing. In this work we propose a method for generating explanations for the goal selection, which can be given to both to intelligent agents and human users.

An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents

Planning problems with continuous state and action spacesare difficult to solve with existing planning techniques, specially when the state transition is defined by a high-dimension non-linear dynamics. Recently, a technique called Planning through Backpropagation (PtB) was introduced as an efficient and scalable alternative to traditional optimization-based methods for continuous planning problems. PtB leverages modern gradient descent algorithms and highly optimized automatic differentiation libraries to obtain approximate solutions. However, to date there have been no empirical evaluations comparing PtB with Linear-Quadratic (LQ) control problems. In this work, we compare PtB with an optimal algorithm from control theory called LQR, and its iterative version iLQR, when solving linear and non-linear continuous deterministic planning problems. The empirical results suggest that PtB can be an efficient alternative to optimizing non-linear continuous deterministic planning, being much easier to be implemented and stabilized than classical model-predictive control methods.

On the Performance of Planning throughBackpropagation

An important and often neglected aspect in probabilistic planning is how to account for different attitudes towards risk in the process. In goal-driven problems, modeled as Shortest Stochastic Path (SSP) problems, risk arises from the uncertainties on future events and how they can lead to goal states. An SSP agent that minimizes the expected accumulated cost is considered a risk-neutral agent, while with a different optimization criterion it could choose between two extreme attitudes: risk-aversion or risk-prone. In this work we consider a Risk Sensitive ssp (called RS-SSP) that uses an expected exponential utility parameterized by the risk factor lambda that is used to define the agent's risk attitude. Moreover, a lambda-value is feasible if it admits a policy with finite expected cost. There are several algorithms capable of determining an optimal policy for rs-ssps when we fix a feasible value for lambda. However, so far, there has been only one approach to find an extreme lambda feasible i.e., an extreme risk-averse policy. In this work we propose and compare new approaches to finding the extreme feasible lambda value for a given RS-SSP, and to return the corresponding extreme risk-averse policy. Experiments on three benchmark domains show that our proposals outperform previous approach, allowing the solution of larger problems

Finding Feasible Policies for Extreme Risk-Averse Agents in Probabilistic Planning

Ensembles of classifiers are a very popular type of method for performing classification, due to their usually high predictive accuracy. However, ensembles have two drawbacks. First, ensembles are usually considered a 'black box', non-interpretable type of classification model, mainly because typically there are a very large number of classifiers in the ensemble (and often each classifier in the ensemble is a black-box classifier by itself). This lack of interpretability is an important limitation in application domains where a model's predictions should be carefully interpreted by users, like medicine, law, etc. Second, ensemble methods typically involve many hyper-parameters, and it is difficult for users to select the best settings for those hyper-parameters. In this work we propose an Evolutionary Algorithm (an Estimation of Distribution Algorithm) that addresses both these drawbacks. This algorithm optimizes the hyper-parameter settings of a small ensemble of 5 interpretable classifiers, which allows users to interpret each classifier. 
In our experiments, the ensembles learned by the proposed Evolutionary Algorithm achieved the same level of predictive accuracy as a well-known Random Forest ensemble, but with the benefit of learning interpretable models (unlike Random Forests).

An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers

Image classification is a well-established problem in computer vision. Most state-of-the-art models rely on Convolutional Neural Networks to achieve near-human performance in that task. However, CNNs have shown to be susceptible to image manipulation, which undermines the trustability of perception systems. This property is critical, especially in unmanned systems, autonomous vehicles, and scenarios where light cannot be controlled. We investigate the robustness of several Deep-Learning based image recognition models and how the accuracy is affected by several distinct image distortions. The distortions include ill-exposure, low-range image sensors, and common noise types. Furthermore, we also propose and evaluate an image pipeline designed to minimize image distortion before the image classification is performed. Results show that most CNN models are marginally affected by mild miss-exposure and Shot noise. On the one hand, the proposed pipeline can provide significant gain on miss-exposed images. On the other hand, harsh miss-exposure, signal-dependent noise, and impulse noise incur in a high impact on all evaluated models.

A Pipelined Approach to Deal with Image Distortion in Computer Vision

Image Enhancement is one of the most important phases of the image processing system. Contrast Enhancement plays a key role in this step. Histogram Equalization (HE) is one of the main tools used to improve the contrast of an image. However, the use of HE causes an increase in the natural brightness of the image, which is not desirable in many types of applications such as consumer electronics products. This presentation will show a variation of the Differential Evolution metaheuristic algorithm for Contrast Optimization, called DECO, and the results of the evaluation fo the proposed algorithm.

A Differential Evolution Algorithm for Contrast Optimization

Classification is an essential task in the field of MachineLearning, where developing a classifier that minimizes errors on unknown data is one of its central problems. It is known that the analytic centeris a good approximation of the center of mass of the version space that is consistent with the Bayes-optimal decision surface. Therefore, in this work, we propose an evolutionary algorithm, relying on the convexity properties of the version space, that evolves a population of perceptron classifiers in order to find a solution that approximates its analytic cen-ter. Hyperspherical coordinates are used to guarantee feasibility when generating new individuals and enabling exploration to be uniformly distributed through the search space. To evaluate the individuals we consider using a potential function that employs a logarithmic barrier penalty. Experiments were performed on real datasets, and the obtained results indicate concrete possibilities for applying the proposed algorithm for solving practical problems.

An Evolutionary Analytic Center Classifier

In the Multi-Agent Pickup and Delivery (MAPD) problem, agents must process a sequence of tasks that may appear in the system in different time-steps. Commonly, this problem has two parts: (i) task allocation, where the agent receives the appropriate task, and (ii) path planning, where the best path for the agent to perform its task, without colliding with other agents, is defined. In this work, we propose an integer-encoded genetic algorithm for solving the task allocation part of the MAPD problem combined with two-path planning algorithms already known in the literature: the Prioritized Planning and the Improved Conflict-Based Search (ICBS). Computational experiments were carried out with different numbers of agents and the frequency of tasks. The results show that the proposed approach achieves better results for large instances when compared to another technique from the literature.

Solving Multi-Agent Pickup and Delivery Problems Using a Genetic Algorithm

Basically, the presentation consists of contextualizing and motivating the work done, describing the objectives and contributions. Then, the methodology is approached together with some comparisons with the related works. Next, the experiments and results are commented with a brief discussion. By the end, the conclusion and future work are presented. Finally, a short video is presented showing the performance of the best agent of our work.

Improving FIFA Player Agents Decision-Making Architectures based on Convolutional Neural Networks through Evolutionary Techniques

Among the existing techniques to improve the performance of metaheuristics in optimization problems, adaptive parameter control consists in varying one or more parameters of a given metaheuristic according to some indicator of the search conditions. This approach allows metaheuristics to change algorithmic behaviour during the search, and is particularly relevant for the optimization of dynamic problems. In this research we theoretically analyse in which ways the parameters of the ant colony optimization for continuous domains metaheuristic can be adapted, regarding how each parameter influences exploration and exploitation characteristics of the algorithm. Our experimental contributions include validating the colony success rate as a search condition estimator and choosing suitable maps from this estimator to the parameters q and ksi of the algorithm. Beyond that, we compare the performances of three proposed adaptive versions of the base metaheuristic and show the benefits of simultaneously adapting multiple parameters.

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