The range of applications that require processing of temporally and spatially distributed sensory data is expanding. Common challenges in domains with these characteristics are sound reasoning about uncertain phenomena and coping with the dynamic nature of processes that influence these phenomena. To address these challenges we propose the use of causal Bayesian Networks for probabilistic reasoning and introduce the Logical OR gate in order to combine them with dynamic processes estimated by arbitrary Markov processes. To illustrate the genericness of the proposed approach, we apply it in a wildlife protection use case. Furthermore we show that the resulting model supports modularization of computations, which allows for efficient decentralized processing.
Reference:
Claessens, R., De Waal, A., De Villiers, P. et al. 2016. Bayesian inference in dynamic domains using logical OR gates. Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS), 25-28 April 2016, Rome, Italy, p. 134-142. DOI: 10.5220/0005768601340142
Claessens, R., De Waal, A., De Villiers, P., Penders, A., Pavlin, G., & Tuyls, K. (2016). Bayesian inference in dynamic domains using logical OR gates. SCITEPRESS. http://hdl.handle.net/10204/9349
Claessens, R, A De Waal, Pieter De Villiers, A Penders, G Pavlin, and K Tuyls. "Bayesian inference in dynamic domains using logical OR gates." (2016): http://hdl.handle.net/10204/9349
Claessens R, De Waal A, De Villiers P, Penders A, Pavlin G, Tuyls K, Bayesian inference in dynamic domains using logical OR gates; SCITEPRESS; 2016. http://hdl.handle.net/10204/9349 .
