Modelo de Fusão de Dados com Incerteza para Consciência Situacional
Resumo
A área de Consciência Situacional provê uma teoria que embasa a tomada de decisão em agentes, de forma a permitir a percepção e a compreensão do ambiente em que o agente está inserido. Contudo, a transformação de estímulos sensoriais em crenças que favoreçam o ciclo de raciocínio em agentes BDI ainda é uma área pouco explorada. Este trabalho apresenta um modelo para geração de crenças, utilizando inferência Fuzzy-Bayesiana. Para ilustrar a proposta, um exemplo em navegação e localização robótica é utilizado.Referências
Brignoli, J. T., Pires, M. M., Nassar, S. M., and Sell, D. (2015). A fuzzy-Bayesian model based on the superposition of states applied to the clinical reasoning support. IntelliSys 2015 - Proceedings of 2015 SAI Intelligent Systems Conference, pages 210–219.
Chen, L., Nugent, C., and Al-Bashrawi, A. (2009). Semantic Data Management for Situation-aware Assistance in Ambient Assisted Living. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications &Amp; Services, iiWAS ’09, pages 298–305, New York, NY, USA. ACM.
Endsley, M. R. (1995). Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1):32–64.
Golestan, K., Soua, R., Karray, F., and Kamel, M. S. (2016). Situation awareness within the context of connected cars: A comprehensive review and recent trends. Information Fusion, 29:68–83.
Insaurralde, C. C. and Petillot, Y. R. (2015). Capability-oriented robot architecture for maritime autonomy. Robotics and Autonomous Systems, 67:87–104.
Khaleghi, B., Khamis, A., Karray, F. O., and Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1):28–44.
Kokar, M. M., Matheus, C. J., and Baclawski, K. (2009). Ontology-based situation awareness. Information Fusion, 10(1):83–98.
Koller, D. and Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.
Steinberg, A. N. and Bowman, C. L. (2004). Rethinking the JDL Data Fusion Levels. NSSDF Conference Proceedings, (c):1–18.
Viertl, R. (1987). Is it Necessary to Develop a Fuzzy Bayesian Inference ? In Viertl, R., editor, Probability and Bayesian Statistics, pages 471–475. Springer US, Boston, MA.
Viertl, R. (1989). Modeling of Fuzzy Measurements in Reliability Estimation.
Viertl, R. (1995). Statistics with Fuzzy Data. In Della Riccia, G., Kruse, R., and Viertl, R., editors, Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence, pages 33–49, Vienna. Springer Vienna.
Viertl, R. (2008). Fuzzy Bayesian Inference. Smps, pages 10–15.
Viertl, R. and Hule, H. (1991). On Bayes ’ theorem for fuzzy data. Statistical Papers, 32:115–122.
White, F. E. (1988). A model for data fusion. In Proc. 1st National Symposium on Sensor Fusion, volume 2, pages 149–158.
Wickens, C. D. and Hollands, J. G. (2000). Engineering Psychology and Human Performance. Prentice Hall, Upper Saddle River, New Jersey, 3 edition.
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3):338–353.
Chen, L., Nugent, C., and Al-Bashrawi, A. (2009). Semantic Data Management for Situation-aware Assistance in Ambient Assisted Living. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications &Amp; Services, iiWAS ’09, pages 298–305, New York, NY, USA. ACM.
Endsley, M. R. (1995). Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1):32–64.
Golestan, K., Soua, R., Karray, F., and Kamel, M. S. (2016). Situation awareness within the context of connected cars: A comprehensive review and recent trends. Information Fusion, 29:68–83.
Insaurralde, C. C. and Petillot, Y. R. (2015). Capability-oriented robot architecture for maritime autonomy. Robotics and Autonomous Systems, 67:87–104.
Khaleghi, B., Khamis, A., Karray, F. O., and Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1):28–44.
Kokar, M. M., Matheus, C. J., and Baclawski, K. (2009). Ontology-based situation awareness. Information Fusion, 10(1):83–98.
Koller, D. and Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.
Steinberg, A. N. and Bowman, C. L. (2004). Rethinking the JDL Data Fusion Levels. NSSDF Conference Proceedings, (c):1–18.
Viertl, R. (1987). Is it Necessary to Develop a Fuzzy Bayesian Inference ? In Viertl, R., editor, Probability and Bayesian Statistics, pages 471–475. Springer US, Boston, MA.
Viertl, R. (1989). Modeling of Fuzzy Measurements in Reliability Estimation.
Viertl, R. (1995). Statistics with Fuzzy Data. In Della Riccia, G., Kruse, R., and Viertl, R., editors, Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence, pages 33–49, Vienna. Springer Vienna.
Viertl, R. (2008). Fuzzy Bayesian Inference. Smps, pages 10–15.
Viertl, R. and Hule, H. (1991). On Bayes ’ theorem for fuzzy data. Statistical Papers, 32:115–122.
White, F. E. (1988). A model for data fusion. In Proc. 1st National Symposium on Sensor Fusion, volume 2, pages 149–158.
Wickens, C. D. and Hollands, J. G. (2000). Engineering Psychology and Human Performance. Prentice Hall, Upper Saddle River, New Jersey, 3 edition.
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3):338–353.
Publicado
02/05/2018
Como Citar
MITTELMANN, Munyque; MARCHI, Jerusa; WANGENHEIM, Aldo von.
Modelo de Fusão de Dados com Incerteza para Consciência Situacional. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 12. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 109-120.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2018.33259.
