23 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
Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs
Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI‐based approach for disaster risk analysis that we as a society must be cautious of?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/1/risa13476_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/2/risa13476.pd
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
Deep learning has become an increasingly common technique for various control
problems, such as robotic arm manipulation, robot navigation, and autonomous
vehicles. However, the downside of using deep neural networks to learn control
policies is their opaque nature and the difficulties of validating their
safety. As the networks used to obtain state-of-the-art results become
increasingly deep and complex, the rules they have learned and how they operate
become more challenging to understand. This presents an issue, since in
safety-critical applications the safety of the control policy must be ensured
to a high confidence level. In this paper, we propose an automated black box
testing framework based on adversarial reinforcement learning. The technique
uses an adversarial agent, whose goal is to degrade the performance of the
target model under test. We test the approach on an autonomous vehicle problem,
by training an adversarial reinforcement learning agent, which aims to cause a
deep neural network-driven autonomous vehicle to collide. Two neural networks
trained for autonomous driving are compared, and the results from the testing
are used to compare the robustness of their learned control policies. We show
that the proposed framework is able to find weaknesses in both control policies
that were not evident during online testing and therefore, demonstrate a
significant benefit over manual testing methods.Comment: 2020 IEEE International Conference on Robotics and Automation (ICRA
Estimating uncertainty of earthquake rupture using Bayesian neural network
Bayesian neural networks (BNN) are the probabilistic model that combines the
strengths of both neural network (NN) and stochastic processes. As a result,
BNN can combat overfitting and perform well in applications where data is
limited. Earthquake rupture study is such a problem where data is insufficient,
and scientists have to rely on many trial and error numerical or physical
models. Lack of resources and computational expenses, often, it becomes hard to
determine the reasons behind the earthquake rupture. In this work, a BNN has
been used (1) to combat the small data problem and (2) to find out the
parameter combinations responsible for earthquake rupture and (3) to estimate
the uncertainty associated with earthquake rupture. Two thousand rupture
simulations are used to train and test the model. A simple 2D rupture geometry
is considered where the fault has a Gaussian geometric heterogeneity at the
center, and eight parameters vary in each simulation. The test F1-score of BNN
(0.8334), which is 2.34% higher than plain NN score. Results show that the
parameters of rupture propagation have higher uncertainty than the rupture
arrest. Normal stresses play a vital role in determining rupture propagation
and are also the highest source of uncertainty, followed by the dynamic
friction coefficient. Shear stress has a moderate role, whereas the geometric
features such as the width and height of the fault are least significant and
uncertain