Bayesian Inference in Dynamic Domains using Logical OR Gates

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.

[1]  J. Bromley,et al.  The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning , 2005, Environ. Model. Softw..

[2]  Harald Schaub,et al.  Errors in Planning and Decision‐making and the Nature of Human Information Processing , 1994 .

[3]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[4]  Gregor Pavlin,et al.  Efficient Distributed Bayesian Reasoning via Targeted Instantiation of Variables , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[5]  Mark E. Borsuk,et al.  A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis , 2004 .

[6]  Mark E. Borsuk,et al.  Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian probability network , 2006 .

[7]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[8]  Alta de Waal,et al.  A framework for inferring predictive distributions of rhino poaching events through causal modelling , 2014, 17th International Conference on Information Fusion (FUSION).

[9]  Marinus Maris,et al.  A multi-agent systems approach to distributed bayesian information fusion , 2010, Inf. Fusion.

[10]  K. Mengersen,et al.  Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle , 2010 .

[11]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[12]  D. Pullar,et al.  Using a Bayesian Network in a GIS to Model Relationships and Threats to Koala Populations Close to Urban Environments , 2007 .

[13]  Yang Xiang,et al.  PROBABILISTIC REASONING IN MULTIAGENT SYSTEMS: A GRAPHICAL MODELS APPROACH, by Yang Xiang, Cambridge University Press, Cambridge, 2002, xii + 294 pp., ISBN 0-521-81308-5 (Hardback, £45.00). , 2002, Robotica.

[14]  Sina K. Frank,et al.  A Review of Bayesian Networks as a Participatory Modeling Approach in Support of Sustainable Environmental Management , 2012 .

[15]  Carlos Guestrin,et al.  Robust Probabilistic Inference in Distributed Systems , 2004, UAI.