Comparative Study of Neural Networks Techniques in the Context of Cooperative Observations
Resumo
No problema de Observação Cooperativa de Alvos (CTO), um grupo de observadores em movimento deve monitorar um grupo de alvos em movimento para maximizar o número médio de alvos observados. A maioria das abordagens computacionais do CTO considera que os observadores são agentes racionais e que os alvos são apenas agentes ingênuos. Este trabalho incorpora um modelo de comportamento dos observadores no sistema de tomada de decisões dos alvos, considerando quatro modelos básicos de redes neurais treinadas, para melhorar seu desempenho. Os resultados mostraram que o desempenho da equipe-alvo aumentou quando eles foram modelados como agentes racionais, principalmente quando o modelo incorpora modelos básicos de redes neurais recorrentes em comparação com as abordagens clássicas de feed-forward.
Palavras-chave:
Observação Cooperativa de Alvos, Agentes Inteligentes, Sistemas Multiagentes, Redes Neurais Artificiais
Referências
Bergmeir, C. (2018). Neural networks using the stuttgart neural network simulator (snns).
Braga, A. P., Ludermir, T. B., and Carvalho, A. C. L. (2000). Redes Neurais Artificiais – Teoria e Aplicações.
França, T., Leite, J. L. A., Junior, R. J. C. F., da Costa, L. F., de Souza, R. P., Andrade, J. P. B., and de Campos, G. A. L. (2019). Smart targets to avoid observation in cto problem. Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
K. Kanistras, G. Martins, M. J. R. and Valavanis, K. P. (2013). A survey of unmanned aerial vehicles (uavs) for traffic monitoring. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS).
Luke, S., Sullivan, K., Panait, L., and Balan, G. (2005). Tunably decentralized algorithms for cooperative target observation. pages 911–917.
Parker, L. E. (1999). Cooperative robotics for multi-target observation. intelligent automation soft computing.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-070.
Rashi Aswani, S. K. M. and Paruchuri, P. (2017). Improving surveillance using cooperative target observation. Thirty-First AAAI Conference on Artificial Intelligence.
Riedmiller, M. and Braun, H. (1992). Rprop-a fast adaptive learning algorithm. ISCIS.
Russell, S. and Norvig, P. (2009). Artificial Intelligence: A Modern Approach. Prentice Hall Press, Upper Saddle River, NJ, USA, 3rd edition.
S. Andrew Gadsden Stephanie Bonadies, A. L. (2016). A survey of unmanned ground vehicles with applications to agricultural and environmental sensing.
Sullivan, K. M. and Luke, S. (2004). Autonomous uuv control via tunably decentralized algorithms. 2004 IEEE/OES Autonomous Underwater Vehicles, IEEE Cat:47–53.
Symeonidis, A. and Mitkas, P. (2005). A methodology for predicting agent behavior by the use of data mining techniques. International Workshop on Autonomous Intelligent Systems: Agents and Data Mining.
Veras, C. V. A. (2013). Estudo comparativo de técnicas de redes neurais artificiais na previsão da velocidade do vento em curto prazo.
Wilensky, U. (1999). Netlogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Bergmeir, C. (2018). Neural networks using the stuttgart neural network simulator (snns).
Braga, A. P., Ludermir, T. B., and Carvalho, A. C. L. (2000). Redes Neurais Artificiais – Teoria e Aplicações.
França, T., Leite, J. L. A., Junior, R. J. C. F., da Costa, L. F., de Souza, R. P., Andrade, J. P. B., and de Campos, G. A. L. (2019). Smart targets to avoid observation in cto problem. Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
K. Kanistras, G. Martins, M. J. R. and Valavanis, K. P. (2013). A survey of unmanned aerial vehicles (uavs) for traffic monitoring. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS).
Luke, S., Sullivan, K., Panait, L., and Balan, G. (2005). Tunably decentralized algorithms for cooperative target observation. pages 911–917.
Parker, L. E. (1999). Cooperative robotics for multi-target observation. intelligent automation soft computing.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-070.
Rashi Aswani, S. K. M. and Paruchuri, P. (2017). Improving surveillance using cooperative target observation. Thirty-First AAAI Conference on Artificial Intelligence.
Riedmiller, M. and Braun, H. (1992). Rprop-a fast adaptive learning algorithm. ISCIS.
Russell, S. and Norvig, P. (2009). Artificial Intelligence: A Modern Approach. Prentice Hall Press, Upper Saddle River, NJ, USA, 3rd edition.
S. Andrew Gadsden Stephanie Bonadies, A. L. (2016). A survey of unmanned ground vehicles with applications to agricultural and environmental sensing.
Sullivan, K. M. and Luke, S. (2004). Autonomous uuv control via tunably decentralized algorithms. 2004 IEEE/OES Autonomous Underwater Vehicles, IEEE Cat:47–53.
Symeonidis, A. and Mitkas, P. (2005). A methodology for predicting agent behavior by the use of data mining techniques. International Workshop on Autonomous Intelligent Systems: Agents and Data Mining.
Veras, C. V. A. (2013). Estudo comparativo de técnicas de redes neurais artificiais na previsão da velocidade do vento em curto prazo.
Wilensky, U. (1999). Netlogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Braga, A. P., Ludermir, T. B., and Carvalho, A. C. L. (2000). Redes Neurais Artificiais – Teoria e Aplicações.
França, T., Leite, J. L. A., Junior, R. J. C. F., da Costa, L. F., de Souza, R. P., Andrade, J. P. B., and de Campos, G. A. L. (2019). Smart targets to avoid observation in cto problem. Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
K. Kanistras, G. Martins, M. J. R. and Valavanis, K. P. (2013). A survey of unmanned aerial vehicles (uavs) for traffic monitoring. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS).
Luke, S., Sullivan, K., Panait, L., and Balan, G. (2005). Tunably decentralized algorithms for cooperative target observation. pages 911–917.
Parker, L. E. (1999). Cooperative robotics for multi-target observation. intelligent automation soft computing.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-070.
Rashi Aswani, S. K. M. and Paruchuri, P. (2017). Improving surveillance using cooperative target observation. Thirty-First AAAI Conference on Artificial Intelligence.
Riedmiller, M. and Braun, H. (1992). Rprop-a fast adaptive learning algorithm. ISCIS.
Russell, S. and Norvig, P. (2009). Artificial Intelligence: A Modern Approach. Prentice Hall Press, Upper Saddle River, NJ, USA, 3rd edition.
S. Andrew Gadsden Stephanie Bonadies, A. L. (2016). A survey of unmanned ground vehicles with applications to agricultural and environmental sensing.
Sullivan, K. M. and Luke, S. (2004). Autonomous uuv control via tunably decentralized algorithms. 2004 IEEE/OES Autonomous Underwater Vehicles, IEEE Cat:47–53.
Symeonidis, A. and Mitkas, P. (2005). A methodology for predicting agent behavior by the use of data mining techniques. International Workshop on Autonomous Intelligent Systems: Agents and Data Mining.
Veras, C. V. A. (2013). Estudo comparativo de técnicas de redes neurais artificiais na previsão da velocidade do vento em curto prazo.
Wilensky, U. (1999). Netlogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Bergmeir, C. (2018). Neural networks using the stuttgart neural network simulator (snns).
Braga, A. P., Ludermir, T. B., and Carvalho, A. C. L. (2000). Redes Neurais Artificiais – Teoria e Aplicações.
França, T., Leite, J. L. A., Junior, R. J. C. F., da Costa, L. F., de Souza, R. P., Andrade, J. P. B., and de Campos, G. A. L. (2019). Smart targets to avoid observation in cto problem. Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
K. Kanistras, G. Martins, M. J. R. and Valavanis, K. P. (2013). A survey of unmanned aerial vehicles (uavs) for traffic monitoring. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS).
Luke, S., Sullivan, K., Panait, L., and Balan, G. (2005). Tunably decentralized algorithms for cooperative target observation. pages 911–917.
Parker, L. E. (1999). Cooperative robotics for multi-target observation. intelligent automation soft computing.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-070.
Rashi Aswani, S. K. M. and Paruchuri, P. (2017). Improving surveillance using cooperative target observation. Thirty-First AAAI Conference on Artificial Intelligence.
Riedmiller, M. and Braun, H. (1992). Rprop-a fast adaptive learning algorithm. ISCIS.
Russell, S. and Norvig, P. (2009). Artificial Intelligence: A Modern Approach. Prentice Hall Press, Upper Saddle River, NJ, USA, 3rd edition.
S. Andrew Gadsden Stephanie Bonadies, A. L. (2016). A survey of unmanned ground vehicles with applications to agricultural and environmental sensing.
Sullivan, K. M. and Luke, S. (2004). Autonomous uuv control via tunably decentralized algorithms. 2004 IEEE/OES Autonomous Underwater Vehicles, IEEE Cat:47–53.
Symeonidis, A. and Mitkas, P. (2005). A methodology for predicting agent behavior by the use of data mining techniques. International Workshop on Autonomous Intelligent Systems: Agents and Data Mining.
Veras, C. V. A. (2013). Estudo comparativo de técnicas de redes neurais artificiais na previsão da velocidade do vento em curto prazo.
Wilensky, U. (1999). Netlogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Publicado
15/10/2019
Como Citar
COSTA, Leonardo; ARAÚJO, Matheus; SILVA, Thayanne; F. JUNIOR, Raimundo; ANDRADE, João; CAMPOS, Gustavo.
Comparative Study of Neural Networks Techniques in the Context of Cooperative Observations. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 563-574.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9315.
