11,049 research outputs found
Formal Verification of Input-Output Mappings of Tree Ensembles
Recent advances in machine learning and artificial intelligence are now being
considered in safety-critical autonomous systems where software defects may
cause severe harm to humans and the environment. Design organizations in these
domains are currently unable to provide convincing arguments that their systems
are safe to operate when machine learning algorithms are used to implement
their software.
In this paper, we present an efficient method to extract equivalence classes
from decision trees and tree ensembles, and to formally verify that their
input-output mappings comply with requirements. The idea is that, given that
safety requirements can be traced to desirable properties on system
input-output patterns, we can use positive verification outcomes in safety
arguments. This paper presents the implementation of the method in the tool
VoTE (Verifier of Tree Ensembles), and evaluates its scalability on two case
studies presented in current literature.
We demonstrate that our method is practical for tree ensembles trained on
low-dimensional data with up to 25 decision trees and tree depths of up to 20.
Our work also studies the limitations of the method with high-dimensional data
and preliminarily investigates the trade-off between large number of trees and
time taken for verification
Formal Probabilistic Analysis of a Wireless Sensor Network for Forest Fire Detection
Wireless Sensor Networks (WSNs) have been widely explored for forest fire
detection, which is considered a fatal threat throughout the world. Energy
conservation of sensor nodes is one of the biggest challenges in this context
and random scheduling is frequently applied to overcome that. The performance
analysis of these random scheduling approaches is traditionally done by
paper-and-pencil proof methods or simulation. These traditional techniques
cannot ascertain 100% accuracy, and thus are not suitable for analyzing a
safety-critical application like forest fire detection using WSNs. In this
paper, we propose to overcome this limitation by applying formal probabilistic
analysis using theorem proving to verify scheduling performance of a real-world
WSN for forest fire detection using a k-set randomized algorithm as an energy
saving mechanism. In particular, we formally verify the expected values of
coverage intensity, the upper bound on the total number of disjoint subsets,
for a given coverage intensity, and the lower bound on the total number of
nodes.Comment: In Proceedings SCSS 2012, arXiv:1307.802
A Review of Formal Methods applied to Machine Learning
We review state-of-the-art formal methods applied to the emerging field of
the verification of machine learning systems. Formal methods can provide
rigorous correctness guarantees on hardware and software systems. Thanks to the
availability of mature tools, their use is well established in the industry,
and in particular to check safety-critical applications as they undergo a
stringent certification process. As machine learning is becoming more popular,
machine-learned components are now considered for inclusion in critical
systems. This raises the question of their safety and their verification. Yet,
established formal methods are limited to classic, i.e. non machine-learned
software. Applying formal methods to verify systems that include machine
learning has only been considered recently and poses novel challenges in
soundness, precision, and scalability.
We first recall established formal methods and their current use in an
exemplar safety-critical field, avionic software, with a focus on abstract
interpretation based techniques as they provide a high level of scalability.
This provides a golden standard and sets high expectations for machine learning
verification. We then provide a comprehensive and detailed review of the formal
methods developed so far for machine learning, highlighting their strengths and
limitations. The large majority of them verify trained neural networks and
employ either SMT, optimization, or abstract interpretation techniques. We also
discuss methods for support vector machines and decision tree ensembles, as
well as methods targeting training and data preparation, which are critical but
often neglected aspects of machine learning. Finally, we offer perspectives for
future research directions towards the formal verification of machine learning
systems
Crisis Analytics: Big Data Driven Crisis Response
Disasters have long been a scourge for humanity. With the advances in
technology (in terms of computing, communications, and the ability to process
and analyze big data), our ability to respond to disasters is at an inflection
point. There is great optimism that big data tools can be leveraged to process
the large amounts of crisis-related data (in the form of user generated data in
addition to the traditional humanitarian data) to provide an insight into the
fast-changing situation and help drive an effective disaster response. This
article introduces the history and the future of big crisis data analytics,
along with a discussion on its promise, challenges, and pitfalls
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