529 research outputs found
Robustness of 3D Deep Learning in an Adversarial Setting
Understanding the spatial arrangement and nature of real-world objects is of
paramount importance to many complex engineering tasks, including autonomous
navigation. Deep learning has revolutionized state-of-the-art performance for
tasks in 3D environments; however, relatively little is known about the
robustness of these approaches in an adversarial setting. The lack of
comprehensive analysis makes it difficult to justify deployment of 3D deep
learning models in real-world, safety-critical applications. In this work, we
develop an algorithm for analysis of pointwise robustness of neural networks
that operate on 3D data. We show that current approaches presented for
understanding the resilience of state-of-the-art models vastly overestimate
their robustness. We then use our algorithm to evaluate an array of
state-of-the-art models in order to demonstrate their vulnerability to
occlusion attacks. We show that, in the worst case, these networks can be
reduced to 0% classification accuracy after the occlusion of at most 6.5% of
the occupied input space.Comment: 10 pages, 8 figures, 1 tabl
Probabilistic Interval Temporal Logic and Duration Calculus with Infinite Intervals: Complete Proof Systems
The paper presents probabilistic extensions of interval temporal logic (ITL)
and duration calculus (DC) with infinite intervals and complete Hilbert-style
proof systems for them. The completeness results are a strong completeness
theorem for the system of probabilistic ITL with respect to an abstract
semantics and a relative completeness theorem for the system of probabilistic
DC with respect to real-time semantics. The proposed systems subsume
probabilistic real-time DC as known from the literature. A correspondence
between the proposed systems and a system of probabilistic interval temporal
logic with finite intervals and expanding modalities is established too.Comment: 43 page
Multi-Objective Model Checking of Markov Decision Processes
We study and provide efficient algorithms for multi-objective model checking
problems for Markov Decision Processes (MDPs). Given an MDP, M, and given
multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and
probabilities r\_i \epsilon [0,1], i=1,...,k, we ask whether there exists a
strategy \sigma for the controller such that, for all i, the probability that a
trajectory of M controlled by \sigma satisfies \varphi\_i is at least r\_i. We
provide an algorithm that decides whether there exists such a strategy and if
so produces it, and which runs in time polynomial in the size of the MDP. Such
a strategy may require the use of both randomization and memory. We also
consider more general multi-objective \omega -regular queries, which we
motivate with an application to assume-guarantee compositional reasoning for
probabilistic systems.
Note that there can be trade-offs between different properties: satisfying
property \varphi\_1 with high probability may necessitate satisfying \varphi\_2
with low probability. Viewing this as a multi-objective optimization problem,
we want information about the "trade-off curve" or Pareto curve for maximizing
the probabilities of different properties. We show that one can compute an
approximate Pareto curve with respect to a set of \omega -regular properties in
time polynomial in the size of the MDP.
Our quantitative upper bounds use LP methods. We also study qualitative
multi-objective model checking problems, and we show that these can be analysed
by purely graph-theoretic methods, even though the strategies may still require
both randomization and memory.Comment: 21 pages, 2 figure
Automated Verification of Quantitative Properties of Cardiac Pacemaker Software
This poster paper reports on a model-based framework for software quality assurance for cardiac pacemakers developed in Simulink and described in [Chen/Diciolla/Kwiatkowska/Mereacre - Information&Computation, 2013]. A novel hybrid heart model is proposed that is suitable for quantitative verification of pacemakers.
The heart model is formulated at the level of cardiac cells, can be adapted to patient data, and incorporates stochasticity. We validate the model by demonstrating that its composition with a pacemaker model can be used to check safety properties by means of approximate probabilistic verification
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees
Linear Temporal Logic (LTL) is widely used to specify high-level objectives
for system policies, and it is highly desirable for autonomous systems to learn
the optimal policy with respect to such specifications. However, learning the
optimal policy from LTL specifications is not trivial. We present a model-free
Reinforcement Learning (RL) approach that efficiently learns an optimal policy
for an unknown stochastic system, modelled using Markov Decision Processes
(MDPs). We propose a novel and more general product MDP, reward structure and
discounting mechanism that, when applied in conjunction with off-the-shelf
model-free RL algorithms, efficiently learn the optimal policy that maximizes
the probability of satisfying a given LTL specification with optimality
guarantees. We also provide improved theoretical results on choosing the key
parameters in RL to ensure optimality. To directly evaluate the learned policy,
we adopt probabilistic model checker PRISM to compute the probability of the
policy satisfying such specifications. Several experiments on various tabular
MDP environments across different LTL tasks demonstrate the improved sample
efficiency and optimal policy convergence.Comment: Accepted at the International Joint Conference on Artificial
Intelligence 2023 (IJCAI
When to Trust AI: Advances and Challenges for Certification of Neural Networks
Artificial intelligence (AI) has been advancing at a fast pace and it is now
poised for deployment in a wide range of applications, such as autonomous
systems, medical diagnosis and natural language processing. Early adoption of
AI technology for real-world applications has not been without problems,
particularly for neural networks, which may be unstable and susceptible to
adversarial examples. In the longer term, appropriate safety assurance
techniques need to be developed to reduce potential harm due to avoidable
system failures and ensure trustworthiness. Focusing on certification and
explainability, this paper provides an overview of techniques that have been
developed to ensure safety of AI decisions and discusses future challenges
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