61 research outputs found
Goal Directed Conflict Resolution and Policy Refinement
Peer reviewedPostprin
Reputation-based trust evaluations through diversity
Non peer reviewedPostprin
Probabilistic Logic Programming with Beta-Distributed Random Variables
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 for
highly specified and engineered domains, while simultaneously we maintain the
flexibility offered by aProbLog in handling complex relational domains. Our
motivation is that faithfully capturing the distribution of probabilities is
necessary to compute an expected utility for effective decision making under
uncertainty: unfortunately, these probability distributions can be highly
uncertain due to sparse data. To understand and accurately manipulate such
probability distributions we need a well-defined theoretical framework that is
provided by the Beta distribution, which specifies a distribution of
probabilities representing all the possible values of a probability when the
exact value is unknown.Comment: Accepted for presentation at AAAI 201
COIN@AAMAS2015
COIN@AAMAS2015 is the nineteenth edition of the series and the fourteen papers included in these proceedings demonstrate the vitality of the community and will provide the grounds for a solid workshop program and what we expect will be a most enjoyable and enriching debate.Peer reviewe
Trust and obfuscation principles for quality of information in emerging pervasive environments
Non peer reviewedPostprin
Uncertainty-Aware Deep Classifiers using Generative Models
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 output
high uncertainty for the data samples close to class boundaries or from the
outside of the training distribution. These approaches use an auxiliary data
set during training to represent out-of-distribution samples. However,
selection or creation of such an auxiliary data set is non-trivial, especially
for high dimensional data such as images. In this work we develop a novel
neural network model that is able to express both aleatoric and epistemic
uncertainty to distinguish decision boundary and out-of-distribution regions of
the feature space. To this end, variational autoencoders and generative
adversarial networks are incorporated to automatically generate
out-of-distribution exemplars for training. Through extensive analysis, we
demonstrate that the proposed approach provides better estimates of uncertainty
for in- and out-of-distribution samples, and adversarial examples on well-known
data sets against state-of-the-art approaches including recent Bayesian
approaches for neural networks and anomaly detection methods.Comment: This is a post-referred version of a conference paper published in
AAAI 202
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