Standard

Bayesian inference in dynamic domains using Logical OR gates. / Claessens, R.; de Waal, A.; de Villiers, P.; Penders, Ate; Pavlin, G.; Tuyls, Karl.

Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016). ed. / S. Hammoudi; L. Maciaszek; M.M. Missikoff; O. Camp; J. Cordeiro. Vol. 2 2016. p. 134-142.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Claessens, R, de Waal, A, de Villiers, P, Penders, A, Pavlin, G & Tuyls, K 2016, Bayesian inference in dynamic domains using Logical OR gates. in S Hammoudi, L Maciaszek, MM Missikoff, O Camp & J Cordeiro (eds), Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016). vol. 2, pp. 134-142, ICEIS 2016: 18th International Conference on Enterprise Information Systems, Rome, Italy, 25/04/16. https://doi.org/10.5220/0005768601340142

APA

Claessens, R., de Waal, A., de Villiers, P., Penders, A., Pavlin, G., & Tuyls, K. (2016). Bayesian inference in dynamic domains using Logical OR gates. In S. Hammoudi, L. Maciaszek, M. M. Missikoff, O. Camp, & J. Cordeiro (Eds.), Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) (Vol. 2, pp. 134-142) https://doi.org/10.5220/0005768601340142

Vancouver

Claessens R, de Waal A, de Villiers P, Penders A, Pavlin G, Tuyls K. Bayesian inference in dynamic domains using Logical OR gates. In Hammoudi S, Maciaszek L, Missikoff MM, Camp O, Cordeiro J, editors, Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016). Vol. 2. 2016. p. 134-142 https://doi.org/10.5220/0005768601340142

Author

Claessens, R. ; de Waal, A. ; de Villiers, P. ; Penders, Ate ; Pavlin, G. ; Tuyls, Karl. / Bayesian inference in dynamic domains using Logical OR gates. Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016). editor / S. Hammoudi ; L. Maciaszek ; M.M. Missikoff ; O. Camp ; J. Cordeiro. Vol. 2 2016. pp. 134-142

BibTeX

@inproceedings{5fb4c17198f440769cac97c8faf061fb,
title = "Bayesian inference in dynamic domains using Logical OR gates",
abstract = "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.",
keywords = "Artificial Intelligence and Decision Support Systems, Multi-agent Systems, Strategic Decision Support Systems",
author = "R. Claessens and {de Waal}, A. and {de Villiers}, P. and Ate Penders and G. Pavlin and Karl Tuyls",
year = "2016",
doi = "10.5220/0005768601340142",
language = "English",
isbn = "978-989-758-187-8",
volume = "2",
pages = "134--142",
editor = "S. Hammoudi and L. Maciaszek and M.M. Missikoff and O. Camp and J. Cordeiro",
booktitle = "Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016)",

}

RIS

TY - GEN

T1 - Bayesian inference in dynamic domains using Logical OR gates

AU - Claessens, R.

AU - de Waal, A.

AU - de Villiers, P.

AU - Penders, Ate

AU - Pavlin, G.

AU - Tuyls, Karl

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Artificial Intelligence and Decision Support Systems

KW - Multi-agent Systems

KW - Strategic Decision Support Systems

U2 - 10.5220/0005768601340142

DO - 10.5220/0005768601340142

M3 - Conference contribution

SN - 978-989-758-187-8

VL - 2

SP - 134

EP - 142

BT - Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016)

A2 - Hammoudi, S.

A2 - Maciaszek, L.

A2 - Missikoff, M.M.

A2 - Camp, O.

A2 - Cordeiro, J.

ER -

ID: 31671647