Bayesian inference in dynamic domains using Logical OR gates

R. Claessens; A. de Waal; P. de Villiers; Ate Penders; G. Pavlin; Karl Tuyls

doi:10.5220/0005768601340142

Bayesian inference in dynamic domains using Logical OR gates

R. Claessens, A. de Waal, P. de Villiers, Ate Penders, G. Pavlin, Karl Tuyls

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016)
Editors	S. Hammoudi, L. Maciaszek, M.M. Missikoff, O. Camp, J. Cordeiro
Pages	134-142
Volume	2
DOIs	https://doi.org/10.5220/0005768601340142
Publication status	Published - 2016
Event	ICEIS 2016: 18th International Conference on Enterprise Information Systems - Rome, Italy Duration: 25 Apr 2016 → 28 Apr 2016

Conference

Conference	ICEIS 2016: 18th International Conference on Enterprise Information Systems
Abbreviated title	ICEIS 2016
Country/Territory	Italy
City	Rome
Period	25/04/16 → 28/04/16

Keywords

Artificial Intelligence and Decision Support Systems
Multi-agent Systems
Strategic Decision Support Systems

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10.5220/0005768601340142

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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

@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)",

note = "ICEIS 2016: 18th International Conference on Enterprise Information Systems, ICEIS 2016 ; Conference date: 25-04-2016 Through 28-04-2016",

}

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

Bayesian inference in dynamic domains using Logical OR gates. / Claessens, R.; de Waal, A.; de Villiers, P. et al.
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/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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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

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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.

T2 - ICEIS 2016: 18th International Conference on Enterprise Information Systems

Y2 - 25 April 2016 through 28 April 2016

ER -

Bayesian inference in dynamic domains using Logical OR gates

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