4 research outputs found
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning
Although aviation accidents are rare, safety incidents occur more frequently
and require a careful analysis to detect and mitigate risks in a timely manner.
Analyzing safety incidents using operational data and producing event-based
explanations is invaluable to airline companies as well as to governing
organizations such as the Federal Aviation Administration (FAA) in the United
States. However, this task is challenging because of the complexity involved in
mining multi-dimensional heterogeneous time series data, the lack of
time-step-wise annotation of events in a flight, and the lack of scalable tools
to perform analysis over a large number of events. In this work, we propose a
precursor mining algorithm that identifies events in the multidimensional time
series that are correlated with the safety incident. Precursors are valuable to
systems health and safety monitoring and in explaining and forecasting safety
incidents. Current methods suffer from poor scalability to high dimensional
time series data and are inefficient in capturing temporal behavior. We propose
an approach by combining multiple-instance learning (MIL) and deep recurrent
neural networks (DRNN) to take advantage of MIL's ability to learn using weakly
supervised data and DRNN's ability to model temporal behavior. We describe the
algorithm, the data, the intuition behind taking a MIL approach, and a
comparative analysis of the proposed algorithm with baseline models. We also
discuss the application to a real-world aviation safety problem using data from
a commercial airline company and discuss the model's abilities and
shortcomings, with some final remarks about possible deployment directions
Logical Modes of Attack in Argumentation Networks
This paper studies methodologically robust options for giving logical contents
to nodes in abstract argumentation networks. It defines a variety of notions of
attack in terms of the logical contents of the nodes in a network. General properties
of logics are refined both in the object level and in the meta level to suit the needs
of the application. The network-based system improves upon some of the attempts
in the literature to define attacks in terms of defeasible proofs, the so-called rule-
based systems. We also provide a number of examples and consider a rigorous case
study, which indicate that our system does not suffer from anomalies. We define
consequence relations based on a notion of defeat, consider rationality postulates,
and prove that one such consequence relation is consistent