Uncertainty-aware situational understanding

Richard Tomsett; Lance Kaplan; Federico Cerutti; Paul Sullivan; Daniel Vente; Marc Roig Vilamala; Angelika Kimmig; Alun Preece; Murat Şensoy

doi:10.1117/12.2519945

10 May 2019 Uncertainty-aware situational understanding

Richard Tomsett, Lance Kaplan, Federico Cerutti, Paul Sullivan, Daniel Vente, Marc Roig Vilamala, Angelika Kimmig, Alun Preece, Murat Şensoy

Author Affiliations +

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110060L (2019) https://doi.org/10.1117/12.2519945
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. probabilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore consider the case of subjective (uncertain) Bayesian networks. In previous work we notice that when observations are out of the ordinary, confidence decreases because the relevant training data, effective instantiations, to determine the probabilities for unobserved variables, on the basis of the observed variables, is significantly smaller than the size of the training data, the total number of instantiations. It is therefore of primary importance for the ultimate goal of situational understanding to be able to efficiently determine the reasoning paths that lead to low confidence whenever and wherever it occurs: this can guide specific data collection exercises to reduce such an uncertainty. We propose three methods to this end, and we evaluate them on the basis of a case-study developed in collaboration with professional intelligence analysts.

Conference Presentation

Citation Download Citation

Richard Tomsett, Lance Kaplan, Federico Cerutti, Paul Sullivan, Daniel Vente, Marc Roig Vilamala, Angelika Kimmig, Alun Preece, and Murat Şensoy "Uncertainty-aware situational understanding", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060L (10 May 2019); https://doi.org/10.1117/12.2519945

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