dc.contributor.author |
Claessens, R
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|
dc.contributor.author |
De Waal, A
|
|
dc.contributor.author |
De Villiers, Pieter
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|
dc.contributor.author |
Penders, A
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|
dc.contributor.author |
Pavlin, G
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|
dc.contributor.author |
Tuyls, K
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|
dc.date.accessioned |
2017-07-28T09:03:12Z |
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dc.date.available |
2017-07-28T09:03:12Z |
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dc.date.issued |
2016-04 |
|
dc.identifier.citation |
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 |
en_US |
dc.identifier.isbn |
978-989-758-187-8 |
|
dc.identifier.uri |
http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=9/83RBCCGN4=&t=1
|
|
dc.identifier.uri |
DOI: 10.5220/0005768601340142
|
|
dc.identifier.uri |
https://www.researchgate.net/publication/302973796_Bayesian_Inference_in_Dynamic_Domains_using_Logical_OR_Gates
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/9349
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|
dc.description |
Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS), 25-28 April 2016, Rome, Italy |
en_US |
dc.description.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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
SCITEPRESS |
en_US |
dc.relation.ispartofseries |
Worklist;18516 |
|
dc.subject |
Artificial intelligence and decision support systems |
en_US |
dc.subject |
Multi-agent systems |
en_US |
dc.subject |
Strategic decision support systems |
en_US |
dc.title |
Bayesian inference in dynamic domains using logical OR gates |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
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 |
en_ZA |
dc.identifier.chicagocitation |
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 |
en_ZA |
dc.identifier.vancouvercitation |
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 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Claessens, R
AU - De Waal, A
AU - De Villiers, Pieter
AU - Penders, A
AU - Pavlin, G
AU - Tuyls, K
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.
DA - 2016-04
DB - ResearchSpace
DP - CSIR
KW - Artificial intelligence and decision support systems
KW - Multi-agent systems
KW - Strategic decision support systems
LK - https://researchspace.csir.co.za
PY - 2016
SM - 978-989-758-187-8
T1 - Bayesian inference in dynamic domains using logical OR gates
TI - Bayesian inference in dynamic domains using logical OR gates
UR - http://hdl.handle.net/10204/9349
ER -
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en_ZA |