Comparative Study of Neural Networks Techniques in the Context of Cooperative Observations
Abstract
In the Cooperative Target Observation (CTO) problem, a group of moving observers should monitor a group of moving targets to maximize the average number of observed targets. The majority of computational approaches to the CTO considers that the observers are rational agents and that the targets are only naive agents. This work incorporates a model of the observers? behavior in the targets? decision-making system, considering four basic models of neural networks trained, to improve their performance. The results showed that the target team?s performance increased when they were modeled as rational agents, mainly when the model incorporates basic models of recurrent neural networks compared to classic feed-forward approaches.
Keywords:
Cooperative Target Observation, Intelligent Agents, Multiagent Systems, Artificial Neural Networks
References
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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).
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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.
Published
2019-10-15
How to Cite
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: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (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.
