Classifying Organizational Structures on Targets in the Cooperative Target Observation

  • Thayanne Silva Universidade Estadual do Ceará
  • Matheus Araújo Universidade Estadual do Ceará
  • Raimundo F. Junior Universidade Estadual do Ceará
  • Leonardo Costa Universidade Federal do Ceará
  • João Andrade Universidade Estadual do Ceará
  • Gustavo Campos Universidade Estadual do Ceará

Resumo


This paper proposes an approach to classify the organizational structure of a group of moving target agents that are continuously monitored by a smaller group of moving observer agents. The approach considers that the group of target agents can organize themselves according to eight different paradigms. The agents communicate through the exchange of messages whose contents are speech-act performative. We evaluate the approach considering seven techniques to solve the problem of classifying the group of target agents' organizational structure. The results show that the approach is promising, as it obtained a good performance, measured during experiments using agent-based simulations.

Palavras-chave: Cooperative Target Observation, Extension of Target's Strategy, Organization, Machine Learning

Referências

Abbas, H. A., Shaheen, S. I., and Amin, M. H. (2015). Organization of multi-agent systems: an overview. arXiv preprint arXiv:1506.09032.

Andrade, J. P. et al. (2018). Organization/fuzzy approach to the cto problem. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pages 444–449. IEEE.

Aswani, R., Munnangi, S. K., and Paruchuri, P. (2017). Improving surveillance using cooperative target observation. In Thirty-First AAAI Conference on Artificial Intelligence.

Costa, L. et al. (2019). Comparative study of neural networks techniques in the context of cooperative observations. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, pages 563–574. SBC. da Silva, F. et al. (2019). Smart targets to avoid observation in cto problem. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pages 1958–1960.

Kravari, K. and Bassiliades, N. (2015). A survey of agent platforms. Journal of Artificial Societies and Social Simulation, 18(1):11.

Luke, S., Sullivan, K., Panait, L., and Balan, G. (2005). Tunably decentralized algorithms for cooperative target observation. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pages 911–917.

Maronna, R. A., Martin, R. D., Yohai, V. J., et al. (2019). Robust statistics: theory and methods (with R). John Wiley & Sons.

Parker, L. E. (1999). Cooperative robotics for multi-target observation. Intelligent Automation & Soft Computing, 5(1):5–19.

Randles, B. M., Pasquetto, I. V., Golshan, M. S., and Borgman, C. L. (2017). Using the jupyter notebook as a tool for open science: An empirical study. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 1–2. IEEE.

Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.
Publicado
20/10/2020
SILVA, Thayanne; ARAÚJO, Matheus; F. JUNIOR, Raimundo; COSTA, Leonardo; ANDRADE, João; CAMPOS, Gustavo. Classifying Organizational Structures on Targets in the Cooperative Target Observation. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 718-729. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2020.12173.