43 citations · 92 across the 13 of their papers we have counts for
14 papers
Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments
Mateus G. Machado, João G. Melo, Cleber Zanchettin +4
This work investigates the potential of Reinforcement Learning (RL) to tackle robot motion planning challenges in the dynamic RoboCup Small Size League (SSL). Using a heuristic con…
BERT for Long Documents: A Case Study of Automated ICD Coding
Arash Afkanpour, Shabir Adeel, Hansenclever Bassani +11
Transformer models have achieved great success across many NLP problems. However, previous studies in automated ICD coding concluded that these models fail to outperform some of th…
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
Felipe B. Martins, Mateus G. Machado, Hansenclever F. Bassani +2
Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluat…
Topological Semantic Mapping by Consolidation of Deep Visual Features
Ygor C. N. Sousa, Hansenclever F. Bassani
Many works in the recent literature introduce semantic mapping methods that use CNNs (Convolutional Neural Networks) to recognize semantic properties in images. The types of proper…
An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
Carlos H. C. Pena, Mateus G. Machado, Mariana S. Barros +6
The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent wi…
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer
Hansenclever F. Bassani, Renie A. Delgado, José Nilton de O. Lima Junior +5
This article introduces an open framework, called VSSS-RL, for studying Reinforcement Learning (RL) and sim-to-real in robot soccer, focusing on the IEEE Very Small Size Soccer (VS…
Deep Categorization with Semi-Supervised Self-Organizing Maps
Pedro H. M. Braga, Heitor R. Medeiros, Hansenclever F. Bassani
Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Label…
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
Hansenclever F. Bassani, Renie A. Delgado, Jose Nilton de O. Lima Junior +3
This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS), a traditional league in t…
MOEA/D with Uniformly Randomly Adaptive Weights
Lucas R. C. de Farias, Pedro H. M. Braga, Hansenclever F. Bassani +1
When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to…
Self-Organizing Maps with Variable Input Length for Motif Discovery and Word Segmentation
Raphael C. Brito, Hansenclever F. Bassani
Time Series Motif Discovery (TSMD) is defined as searching for patterns that are previously unknown and appear with a given frequency in time series. Another problem strongly relat…
Incremental Semantic Mapping with Unsupervised On-line Learning
Ygor C. N. Sousa, Hansenclever F. Bassani
This paper introduces an incremental semantic mapping approach, with on-line unsupervised learning, based on Self-Organizing Maps (SOM) for robotic agents. The method includes a ma…
A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds
Pedro H. M. Braga, Hansenclever F. Bassani
In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled…
A Semi-Supervised Self-Organizing Map for Clustering and Classification
Pedro H. M. Braga, Hansenclever F. Bassani
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few l…
A Neural Network Architecture for Learning Word-Referent Associations in Multiple Contexts
Hansenclever F. Bassani, Aluizio F. R. Araujo
This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence coll…