Difference between revisions of "User:EwertonLopes"
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== PhD Research == | == PhD Research == | ||
| − | My [[Robogame_Strategy| PhD research project]] proposes to investigate how to develop complex strategy-based abilities in autonomous robots for the purpose of designing better Physically Interactive Robogames (PIRG) by the use of machine learning (ML) techniques. Specifically, I tackle the development of player modelling (which | + | My [[Robogame_Strategy| PhD research project]] proposes to investigate how to develop complex strategy-based abilities in autonomous robots for the purpose of designing better Physically Interactive Robogames (PIRG) by the use of machine learning (ML) techniques. Specifically, I tackle the development of player modelling (which should also include an approach to intention detection) for strategy adjustment with the aim of keeping (or raising) the human player engagement. |
Latest revision as of 14:10, 26 August 2015
Ewerton Lopes
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| E-Mail: | ewerton.lopes@polimi.it |
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| Status: | active |
Hi. I'm a PhD student from Politecnico di Milano (POLIMI). I have a Master of Science degree in informatics from Universidade Federal da Paraíba, in Brazil (2015). I got my major degree (licentiate) in Computer Science from the same university in 2013. Currently at POLIMI, I have the support from the Brazilian National Council for Scientific and Technological Development (CNPq).
Contents
Research Interests
My main research interests are:
- Artificial Intelligence and bio-inspired computational models;
- Probabilistic reasoning and Machine Learning (specially classification models);
- Intelligent autonomous agents;
- Robogames
PhD Research
My PhD research project proposes to investigate how to develop complex strategy-based abilities in autonomous robots for the purpose of designing better Physically Interactive Robogames (PIRG) by the use of machine learning (ML) techniques. Specifically, I tackle the development of player modelling (which should also include an approach to intention detection) for strategy adjustment with the aim of keeping (or raising) the human player engagement.
