Bayesian inference in dynamic domains using Logical OR gates

Conference paper (2016)

Authors

R Claessens University of Liverpool, Thales
A. de Waal University of Pretoria
P. de Villiers University of Pretoria, Council for Scientific and Industrial Research
A.B. Penders Data-Intensive Systems - EEMCS , Thales
G. Pavlin Thales, Universiteit van Amsterdam
K.P. Tuyls Delft Center for Systems and Control , University of Liverpool
Research Group
Data-Intensive Systems (EEMCS) (TU Delft)
Multi-agent Systems Artificial Intelligence and Decision Support Systems Strategic Decision Support Systems
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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Data-Intensive 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.