Uncertainty-aware situational understanding
dc.contributor.author | Tomsett, R. | |
dc.contributor.author | Kaplan, L. | |
dc.contributor.author | Cerutti, F. | |
dc.contributor.author | Sullivan, P. | |
dc.contributor.author | Vente, D. | |
dc.contributor.author | Vilamala, M. R. | |
dc.contributor.author | Kimmig, A. | |
dc.contributor.author | Preece, A. | |
dc.contributor.author | Şensoy, Murat | |
dc.contributor.editor | Pham, T. | |
dc.date.accessioned | 2020-08-26T12:43:59Z | |
dc.date.available | 2020-08-26T12:43:59Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-1-5106-2678-2 | |
dc.identifier.issn | 0277-786X | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/6836 | |
dc.identifier.uri | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11006/2519945/Uncertainty-aware-situational-understanding/10.1117/12.2519945.short?SSO=1 | |
dc.description.abstract | Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. prob-abilistic 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. | en_US |
dc.description.sponsorship | United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence | |
dc.language.iso | eng | en_US |
dc.publisher | SPIE | en_US |
dc.relation.ispartof | Proceedings of SPIE Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications | |
dc.rights | restrictedAccess | |
dc.title | Uncertainty-aware situational understanding | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0001-8806-4508 & YÖK ID 41438) Şensoy, Murat | |
dc.contributor.ozuauthor | Şensoy, Murat | |
dc.identifier.volume | 11006 | en_US |
dc.identifier.wos | WOS:000502074700017 | |
dc.identifier.doi | 10.1117/12.2519945 | en_US |
dc.subject.keywords | Uncertainty aware | en_US |
dc.subject.keywords | Situational understanding | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85072585959 | |
dc.contributor.authorMale | 1 | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff |
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