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Authors: Rik Claessens 1 ; Alta de Waal 2 ; Pieter de Villiers 3 ; Ate Penders 4 ; Gregor Pavlin 5 and Karl Tuyls 6

Affiliations: 1 University of Liverpool and Thales Research & Technology, United Kingdom ; 2 University of Pretoria, South Africa ; 3 University of Pretoria and Council for Scientific and Industrial Research, South Africa ; 4 Thales Research & Technology and Delft University of Technology, Netherlands ; 5 Thales Research & Technology and University of Amsterdam, Netherlands ; 6 University of Liverpool and Delft University of Technology, United Kingdom

ISBN: 978-989-758-187-8

Keyword(s): Artificial Intelligence and Decision Support Systems, Multi-agent Systems, Strategic Decision Support Systems.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Software Engineering ; Strategic Decision Support Systems ; Symbolic Systems

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.

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Paper citation in several formats:
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

@conference{iceis16,
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,},
year={2016},
pages={134-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005768601340142},
isbn={978-989-758-187-8},
}

TY - CONF

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

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