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Uncertainty-aware situational understanding
(SPIE, 2019)
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 ...
FUSE-BEE: Fusion of subjective opinions through behavior estimation
(IEEE, 2015)
Information is critical in almost all decision making processes. Therefore, it is important to get the right information at the right time from the right sources. However, information sources may behave differently while ...
Subjective bayesian networks and human-in-the-loop situational understanding
(Springer, 2018-03-21)
In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well ...
TRIBE: Trust revision for information based on evidence
(IEEE, 2013)
In recent years, the number of information sources available to support decision-making has increased dramatically. However, more information sources do not always mean higher precision in the fused information. This is ...
Trust estimation and fusion of uncertain information by exploiting consistency
(IEEE, 2014)
Agents may cooperate by communicating their opinions about various phenomena. These opinions are then fused by agents and used for informed decision-making. However, fusing opinions from diverse sources is not trivial - ...
Reasoning with uncertain information and trust
(SPIE, 2013)
A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, ...
Trust estimation of sources over correlated propositions
(IEEE, 2018-09-05)
This work analyzes the impact of correlated propositions when estimating the reporting behavior of information sources. These behavior estimates are critical for fusion, and traditional methods assume the propositions are ...
Semantic reasoning with uncertain information from unreliable sources
(Springer International Publishing, 2016)
Intelligent software agents may significantly benefit from semantic reasoning. However, existing semantic reasoners are based on Description Logics, which cannot handle vague, incomplete, and unreliable knowledge. In this ...
Evidential deep learning to quantify classification uncertainty
(Neural Information Processing Systems Foundation, 2018)
Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant ...
SOBE: Source behavior estimation for subjective opinions In multiagent systems
(International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2016)
In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources
of these opinions may not always be competent and honest but more likely erroneous or even ...
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