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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 ...
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 ...
Learning and reasoning in complex coalition information environments: a critical analysis
(IEEE, 2018-09-05)
In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight - i.e., accurate ...
Uncertainty-aware deep classifiers using generative models
(Association for the Advancement of Artificial Intelligence, 2020)
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ...
Probabilistic logic programming with beta-distributed random variables
(Association for the Advancement of Artificial Intelligence, 2019-07-17)
We enable aProbLog-a probabilistic logical programming approach-to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms ...
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