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 volumeConference contributionScientificpeer-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 languageEnglish
Title of host publicationProceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016)
EditorsS. Hammoudi, L. Maciaszek, M.M. Missikoff, O. Camp, J. Cordeiro
Pages134-142
Volume2
DOIs
Publication statusPublished - 2016
EventICEIS 2016: 18th International Conference on Enterprise Information Systems - Rome, Italy
Duration: 25 Apr 201628 Apr 2016

Conference

ConferenceICEIS 2016: 18th International Conference on Enterprise Information Systems
Abbreviated titleICEIS 2016
CountryItaly
CityRome
Period25/04/1628/04/16

Keywords

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

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