1,143 research outputs found
Simulation-based Validation for Autonomous Driving Systems
Simulation is essential to validate autonomous driving systems. However, a
simple simulation, even for an extremely high number of simulated miles or
hours, is not sufficient. We need well-founded criteria showing that simulation
does indeed cover a large fraction of the relevant real-world situations. In
addition, the validation must concern not only incidents, but also the
detection of any type of potentially dangerous situation, such as traffic
violations.
We investigate a rigorous simulation and testing-based validation method for
autonomous driving systems that integrates an existing industrial simulator and
a formally defined testing environment. The environment includes a scenario
generator that drives the simulation process and a monitor that checks at
runtime the observed behavior of the system against a set of system properties
to be validated. The validation method consists in extracting from the
simulator a semantic model of the simulated system including a metric graph,
which is a mathematical model of the environment in which the vehicles of the
system evolve. The monitor can verify properties formalized in a first-order
linear temporal logic and provide diagnostics explaining their non
satisfaction. Instead of exploring the system behavior randomly as many
simulators do, we propose a method to systematically generate sets of scenarios
that cover potentially risky situations, especially for different types of
junctions where specific traffic rules must be respected. We show that the
systematic exploration of risky situations has uncovered many flaws in the real
simulator that would have been very difficult to discover by a random
exploration process
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Exploiting Process Algebras and BPM Techniques for Guaranteeing Success of Distributed Activities
The communications and collaborations among activities, pro-
cesses, or systems, in general, are the base of complex sys-
tems defined as distributed systems. Given the increasing
complexity of their structure, interactions, and functionali-
ties, many research areas are interested in providing mod-
elling techniques and verification capabilities to guarantee
their correctness and satisfaction of properties. In particular,
the formal methods community provides robust verification
techniques to prove system properties. However, most ap-
proaches rely on manually designed formal models, making
the analysis process challenging because it requires an expert
in the field. On the other hand, the BPM community pro-
vides a widely used graphical notation (i.e., BPMN) to design
internal behaviour and interactions of complex distributed
systems that can be enhanced with additional features (e.g.,
privacy technologies). Furthermore, BPM uses process min-
ing techniques to automatically discover these models from
events observation. However, verifying properties and ex-
pected behaviour, especially in collaborations, still needs a
solid methodology.
This thesis aims at exploiting the features of the formal meth-
ods and BPM communities to provide approaches that en-
able formal verification over distributed systems. In this con-
text, we propose two approaches. The modelling-based ap-
proach starts from BPMN models and produces process al-
gebra specifications to enable formal verification of system
properties, including privacy-related ones. The process mining-
based approach starts from logs observations to automati-
xv
cally generate process algebra specifications to enable veri-
fication capabilities
A Last-Level Defense for Application Integrity and Confidentiality
Our objective is to protect the integrity and confidentiality of applications
operating in untrusted environments. Trusted Execution Environments (TEEs) are
not a panacea. Hardware TEEs fail to protect applications against Sybil, Fork
and Rollback Attacks and, consequently, fail to preserve the consistency and
integrity of applications. We introduce a novel system, LLD, that enforces the
integrity and consistency of applications in a transparent and scalable
fashion. Our solution augments TEEs with instantiation control and rollback
protection. Instantiation control, enforced with TEE-supported leases,
mitigates Sybil/Fork Attacks without incurring the high costs of solving
crypto-puzzles. Our rollback detection mechanism does not need excessive
replication, nor does it sacrifice durability. We show that implementing these
functionalities in the LLD runtime automatically protects applications and
services such as a popular DBMS
Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
Prescriptive Process Monitoring systems recommend, during the execution of a
business process, interventions that, if followed, prevent a negative outcome
of the process. Such interventions have to be reliable, that is, they have to
guarantee the achievement of the desired outcome or performance, and they have
to be flexible, that is, they have to avoid overturning the normal process
execution or forcing the execution of a given activity. Most of the existing
Prescriptive Process Monitoring solutions, however, while performing well in
terms of recommendation reliability, provide the users with very specific
(sequences of) activities that have to be executed without caring about the
feasibility of these recommendations. In order to face this issue, we propose a
new Outcome-Oriented Prescriptive Process Monitoring system recommending
temporal relations between activities that have to be guaranteed during the
process execution in order to achieve a desired outcome. This softens the
mandatory execution of an activity at a given point in time, thus leaving more
freedom to the user in deciding the interventions to put in place. Our approach
defines these temporal relations with Linear Temporal Logic over finite traces
patterns that are used as features to describe the historical process data
recorded in an event log by the information systems supporting the execution of
the process. Such encoded log is used to train a Machine Learning classifier to
learn a mapping between the temporal patterns and the outcome of a process
execution. The classifier is then queried at runtime to return as
recommendations the most salient temporal patterns to be satisfied to maximize
the likelihood of a certain outcome for an input ongoing process execution. The
proposed system is assessed using a pool of 22 real-life event logs that have
already been used as a benchmark in the Process Mining community.Comment: 38 pages, 6 figures, 8 table
Runtime Monitoring DNN-Based Perception
Deep neural networks (DNNs) are instrumental in realizing complex perception
systems. As many of these applications are safety-critical by design,
engineering rigor is required to ensure that the functional insufficiency of
the DNN-based perception is not the source of harm. In addition to conventional
static verification and testing techniques employed during the design phase,
there is a need for runtime verification techniques that can detect critical
events, diagnose issues, and even enforce requirements. This tutorial aims to
provide readers with a glimpse of techniques proposed in the literature. We
start with classical methods proposed in the machine learning community, then
highlight a few techniques proposed by the formal methods community. While we
surely can observe similarities in the design of monitors, how the decision
boundaries are created vary between the two communities. We conclude by
highlighting the need to rigorously design monitors, where data availability
outside the operational domain plays an important role
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
Machine learning has made remarkable advancements, but confidently utilising
learning-enabled components in safety-critical domains still poses challenges.
Among the challenges, it is known that a rigorous, yet practical, way of
achieving safety guarantees is one of the most prominent. In this paper, we
first discuss the engineering and research challenges associated with the
design and verification of such systems. Then, based on the observation that
existing works cannot actually achieve provable guarantees, we promote a
two-step verification method for the ultimate achievement of provable
statistical guarantees
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