Bruno Castro da Silva

发表

A task-and-technique centered survey on visual analytics for deep learning model engineering

Jim Tørresen, Alexandru Telea, Rafael Garcia, 2018, Comput. Graph..

RL-CD: Dealing with Non-Stationarity in Reinforcement Learning

Paulo Martins Engel, Ana L. C. Bazzan, Bruno Castro da Silva, 2006, AAAI.

ITSUMO: an Intelligent Transportation System for Urban Mobility

Ana L. C. Bazzan, Bruno Castro da Silva, Robert Junges, 2004, AAMAS '06.

Context-Based Concurrent Experience Sharing in Multiagent Systems pdf

Victor R. Lesser, Bruno Castro da Silva, Chongjie Zhang, 2017, AAMAS.

A Flexible Approach for Designing Optimal Reward Functions

Ana L. C. Bazzan, Bruno Castro da Silva, Ricardo Grunitzki, 2017, AAMAS.

Active Learning of Parameterized Skills

Bruno Castro da Silva, Andrew G. Barto, George Konidaris, 2014, ICML.

Towards Designing Optimal Reward Functions in Multi-Agent Reinforcement Learning Problems

Ana L. C. Bazzan, Bruno Castro da Silva, Ricardo Grunitzki, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

Improving reinforcement learning with context detection

Paulo Martins Engel, Filipo Studzinski Perotto, Ana L. C. Bazzan, 2006, AAMAS '06.

Learning in groups of traffic signals

Ana L. C. Bazzan, Bruno Castro da Silva, Denise de Oliveira, 2010, Eng. Appl. Artif. Intell..

Learning Parameterized Skills

Bruno Castro da Silva, Andrew G. Barto, George Konidaris, 2012, ICML.

Using Topological Statistics to Bias and Accelerate Route Choice: Preliminary Findings in Synthetic and Real-World Road Networks

Ana L. C. Bazzan, Bruno Castro da Silva, Fernando Stefanello, 2016, ATT@IJCAI.

Biasing the behavior of organizationally adept agents: (extended abstract)

Victor R. Lesser, Xiaoqin Zhang, Daniel D. Corkill, 2013, AAMAS.

Energetic Natural Gradient Descent

Bruno Castro da Silva, Philip S. Thomas, Christoph Dann, 2016, ICML.

TD-δπ: a model-free algorithm for efficient exploration

Bruno Castro da Silva, Andrew G. Barto, 2012, AAAI 2012.

Efficient Local Memory Support for Approximate Computing

Luigi Carro, Antonio Carlos Schneider Beck, Bruno Castro da Silva, 2018, 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC).

Dealing with non-stationary environments using context detection

Paulo Martins Engel, Ana L. C. Bazzan, Bruno Castro da Silva, 2006, ICML.

Os novos padrões de segurança em redes wireless

Bruno Castro da Silva, Afonso Comba de Araujo Neto, 2004 .

ITSUMO: An Intelligent Transportation System for Urban Mobility

Ana L. C. Bazzan, Bruno Castro da Silva, Denise de Oliveira, 2004, IICS.

Autonomous learning of multiple, context-dependent tasks pdf

Bruno Castro da Silva, Gianluca Baldassarre, Vieri Giuliano Santucci, 2020, ArXiv.

Distributed constraint propagation for diagnosis of faults in physical processes

Ana L. C. Bazzan, Bruno Castro da Silva, Bruno C. da Silva, 2007, AAMAS '07.

Learning parameterized motor skills on a humanoid robot

Bruno Castro da Silva, Andrew G. Barto, George Konidaris, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

A Methodology for Neural Network Architectural Tuning Using Activation Occurrence Maps

Jim Tørresen, Alexandru Telea, Rafael Garcia, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

On Ensuring that Intelligent Machines Are Well-Behaved pdf

Bruno Castro da Silva, Andrew G. Barto, Philip S. Thomas, 2017, ArXiv.

Autonomous Open-Ended Learning of Interdependent Tasks pdf

Bruno Castro da Silva, Gianluca Baldassarre, Emilio Cartoni, 2019, ArXiv.

TD-DeltaPi: A Model-Free Algorithm for Efficient Exploration

Bruno Castro da Silva, Andrew G. Barto, 2012, AAAI.

Learning to Minimise Regret in Route Choice

Ana L. C. Bazzan, Bruno Castro da Silva, Gabriel de Oliveira Ramos, 2017, AAMAS.

Universal Off-Policy Evaluation pdf

Bruno Castro da Silva, Scott Niekum, Philip S. Thomas, 2021, NeurIPS.