Bayesian Inference in Dynamic Domains using Logical OR Gates

Rik Claessens, Alta de Waal, Pieter de Villiers, Ate Penders, Gregor Pavlin, Karl Tuyls


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.


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Paper Citation

in Harvard Style

Claessens R., de Waal A., de Villiers P., Penders A., Pavlin G. and Tuyls K. (2016). Bayesian Inference in Dynamic Domains using Logical OR Gates . In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-187-8, pages 134-142. DOI: 10.5220/0005768601340142

in Bibtex Style

author={Rik Claessens and Alta de Waal and Pieter de Villiers and Ate Penders and Gregor Pavlin and Karl Tuyls},
title={Bayesian Inference in Dynamic Domains using Logical OR Gates},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Bayesian Inference in Dynamic Domains using Logical OR Gates
SN - 978-989-758-187-8
AU - Claessens R.
AU - de Waal A.
AU - de Villiers P.
AU - Penders A.
AU - Pavlin G.
AU - Tuyls K.
PY - 2016
SP - 134
EP - 142
DO - 10.5220/0005768601340142