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<!DOCTYPE html>
<html>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>HSCC 2020 - Program</title>
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
<link rel="stylesheet" href="styling.css" type="text/css" media="screen" />
<link rel="stylesheet" href="program.css" type="text/css" media="screen" />
</html>
<body>
<header>
<div>
<hgroup>
<h1><a href="index.html">HSCC 2020</a> - Program</h1>
</hgroup>
<nav>
<ul>
<li><a href="#recordings">Virtual Presentations
</li>
<li><a href="#DLtoc">Proceedings</a></li>
<li><a href="#keynote1">Amazon HSCC Keynote: Prof. Dorsa Sadigh</a></li>
<li><a href="#keynote2">CPS-IoT Keynote: Prof. George Pappas</a></li>
</ul>
</nav>
</div>
</header>
<main>
<section id="keynote2" class="keynote">
<h2>CPS-IoT Week Keynote</h2>
<h3>Professor George J. Pappas (The University of Pennsylvania)</h3>
<img src="https://www.seas.upenn.edu/directory/images/photos/full_Pappas.jpg"/>
<h3><strong>Title:</strong> Safe Autonomy with Deep Learning in Feedback Loop
</br>
</br>
<a style="margin: 1em;" href="https://berkeley.box.com/s/i6s07jmtycsyy1ruq72v0s0waro0ocef"/>slides</a>
<a style="margin: 1em;" href="https://berkeley.box.com/s/umf664tows8q4g3xrsum6o9tk7qp87ft"/>video</a>
</h3>
<details open=true>
<summary><strong>Abstract</strong></summary>
<blockquote>
Deep learning has been extremely successful in
computer vision and perception. Inspired by this
success in perceiving environments, deep learning is
now one of the main sensing modalities in autonomous
robots, including driverless cars. The recent success
of deep reinforcement learning in chess or AlphaGo
suggests that robot planning control will soon be
performed by deep learning in a model free manner,
disrupting traditional model-based engineering design.
However, recent crashes in driverless cars as well as
adversarial attacks in deep networks have exposed the
brittleness of deep learning perception which then
leads to catastrophic decisions. There is a
tremendous opportunity for the cyber physical systems
community to embrace these challenges and develop
principles, architectures, and tools to ensure safety
of autonomous systems.
</br></br>
In this talk, I will present our
approach in ensuring the robustness and safety of
autonomous robots that use deep learning as a
perceptual sensor in the feedback loop. Using ideas
from robust control, we develop tools to analyze the
robustness of deep networks that ensure that the
perception of the environment is more
accurate. Critical to our approach is creating
semantic representations of unknown environments while
also quantifying the uncertainty of semantic
maps. Autonomous planning and control need to both
embrace such semantic representations and formally
reason about the environment uncertainty produced by
deep learning the feedback loop, leading to autonomous
robots that operate with prescribed safely in unknown
but learned environments.
</blockquote>
</details>
<details open=true>
<summary><strong>Bio</strong></summary>
<blockquote>
George J. Pappas is the UPS Foundation Professor and Chair
of the Department of Electrical and Systems Engineering at the
University of Pennsylvania. He also holds a secondary appointment in
the Departments of Computer and Information Sciences, and Mechanical
Engineering and Applied Mechanics. He is member of the GRASP Lab and
the PRECISE Center. He has previously served as the Deputy Dean for
Research in the School of Engineering and Applied Science. His
research focuses on control systems, robotics, formal methods and
machine learning for safe and secure cyber-physical systems
applications. He has received various awards such as the Antonio
Ruberti Young Researcher Prize, the George S. Axelby Award, the
O. Hugo Schuck Best Paper Award, the ICCPS Best Paper Award, the NSF
PECASE award, and the George H. Heilmeier Faculty Excellence Award.
He is a Fellow of IEEE and IFAC and was the inaugural steering
committee chair of CPSWEEK. More than thirty alumni of his group are
now faculty in leading universities around the world.
</blockquote>
</details>
</section>
<section id="keynote1" class="keynote">
<h2>Amazon HSCC Keynote</h2>
<h3> Professor Dorsa Sadigh (Stanford University)</h3>
<img src="https://dorsa.fyi/dorsasadigh.jpeg"/>
<h3><strong>Title:</strong>
Human-CPS through the Lens of Learning and Control
</br>
</br>
<a style="margin: 1em;" href="https://dorsa.fyi/files/dorsa_sadigh_HSCC2020.pdf"/>slides</a>
<a style="margin: 1em;" href="https://berkeley.box.com/s/ukpe8ft78nsycyl8i5m00o07z40j6v7n"/>video</a>
</h3>
<details open=true>
<summary><strong>Abstract</strong></summary>
<blockquote>
Machine learning and control theory have made substantial advances in
the field of cyber-physical systems and robotics in the past
decade. However, there are still many challenges remaining when
studying cyber-physical systems that interact with humans, i.e.,
human-CPS. This includes autonomous vehicles that interact with
people, service robots working with their users at homes, assistive
robots helping disabled bodies, or humans interacting with drones or
other autonomous agents in their daily lives. These challenges
introduce an opportunity for developing new learning and control
algorithms to enable safe and efficient interactive autonomy.
</br></br>
In this talk, I will discuss a
journey in formalizing human-CPS
interaction. Specifically, I will first discuss
developing data-efficient techniques to learn
computational models of human behavior. I will
continue with the challenges that arise when agents
(including humans and robots) interact with each
other. Further, I will argue that in many
applications, a full computational human model is not
necessary for seamless and efficient
interaction. Instead, in many collaborative tasks,
conventions —low-dimensional shared representations of
tasks — is sufficient for capturing the interaction
between agents. I will conclude the talk with
challenges around adapting conventions in human-robot
applications such as assistive teleoperation and
collaborative transport.
</blockquote>
</details>
<details open=true>
<summary><strong>Bio</strong></summary>
<blockquote>
Dorsa Sadigh is an assistant professor in Computer Science and
Electrical Engineering at Stanford University. She is a member of the
Stanford AI Lab and the Human-Centered AI Institute. Her research
interests lie in the intersection of robotics, learning, and control
theory. Specifically, she is interested in developing efficient
algorithms for safe, reliable, and adaptive human-robot
interaction. Dorsa has received her doctoral degree in Electrical
Engineering and Computer Sciences (EECS) at UC Berkeley in 2017, and
has received her bachelor’s degree in EECS at UC Berkeley in 2012.
She is awarded the NSF CAREER award, the Google Faculty Award, and the
Amazon Faculty Research Award.
</blockquote>
</details>
</section>
<section id="recordings">
<h1> Virtual Presentations</h1>
<h2>
<a href="https://docs.google.com/document/d/e/2PACX-1vQPwNi9bVRuyV1ADccEPHHJvQKKyMZJCfZzTfrr5_orUsbhJZXUJUhJ33FU8lGKKsoD_bRbAScD7d9S/pub">Schedule
</a>
</h2>
<h3>📹 Recorded live sessions 📹</h3>
<ol>
<li>
<a href="https://berkeley.box.com/s/ukpe8ft78nsycyl8i5m00o07z40j6v7n">
HSCC Keynote.
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/hdhrdii5nvyoqrek67c8ublk5hmzcrtt">
Control for safety.
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/8micl4boy25d6rb2cz9zko05l17yp536">
Hybrid and Autonomous Systems
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/k7je3yqhro789hr1ulgsqzk5snd818z4">
Automotive and Multi-Agent Systems
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/ea4d7agiy8w9x60dbur9fbuob3xrmp4r">
🏆 Awards
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/umf664tows8q4g3xrsum6o9tk7qp87ft">
CPS-IOT Keynote
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/5xosu2aleuuiohiw6gk308dr2cyxvexf">
Reachability
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/p213mvhgtnp9d7tfu6wsrd6kq2v7xg5u">
Linear System Analysis
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/hq6br4ynsxdmftklor314ybd4ymgzct4">
Formal Synthesis and Verification
</a>
</li>
<li>
<a href="https://berkeley.box.com/s/m3jxr28n70qwvnqk2p9ljvltlqlotf49">
Wrap up
</a>
</li>
</ol>
</section>
<section id="DLtoc">
<div id="DLheader">
<h1>Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control
</h1>
<img class="DLlogo" alt="Digital Library logo" height="30" src="https://dl.acm.org/specs/products/acm/releasedAssets/images/footer-logo1.png">
<a class="DLcitLink" title="Go to the ACM Digital Library for additional information about this proceeding" href="https://dl.acm.org/doi/proceedings/10.1145/3365365">
Frontmatter / Full Citation in the ACM Digital Library
</a>
</div>
<div id="DLcontent">
<h2>SESSION: Reachability</h2>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382192">Utilizing dependencies to obtain subsets of reachable sets</a>
</h3>
<ul class="DLauthors">
<li class="nameList">Niklas Kochdumper</li>
<li class="nameList">Bastian Schürmann</li>
<li class="nameList Last">Matthias Althoff</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABwx-CaDTDMGuJQe87lGxL_a/Niklas%20Kochdumper%20-%20HSCC_Slides_1.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAW-qS6CiIwQZZGCDvgH_Ega/Niklas%20Kochdumper%20-%20HSCC_Video_1.mp4?dl=0">video</a>
<div style="display:inline">
<p>Reachability analysis, in general, is a fundamental method that supports formally-correct
synthesis, robust model predictive control, set-based observers, fault detection,
invariant computation, and conformance checking, to name but a few. In many of these
applications, one requires to compute a reachable set starting within a previously
computed reachable set. While it was previously required to re-compute the entire
reachable set, we demonstrate that one can leverage the dependencies of states within
the previously computed set. As a result, we almost instantly obtain an over-approximative
subset of a previously computed reachable set by evaluating analytical maps. The advantages
of our novel method are demonstrated for falsification of systems, optimization over
reachable sets, and synthesizing safe maneuver automata. In all of these applications,
the computation time is reduced significantly.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382194">Reachability analysis for hybrid systems with nonlinear guard sets</a></h3>
<ul class="DLauthors">
<li class="nameList">Niklas Kochdumper</li>
<li class="nameList Last">Matthias Althoff</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAJS8LiEexrV-FeNfHhDbQGa/Niklas%20Kochdumper%20-%20HSCC_Slides_11.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABlUGXs2KMxIQel7-7GuCXua/Niklas%20Kochdumper%20-%20HSCC_Video_11.mp4?dl=0">video</a>
<div style="display:inline">
<p>Reachability analysis is one of the most important methods for formal verification
of hybrid systems. The main difficulty for hybrid system reachability analysis is
to calculate the intersection between reachable set and guard sets. While there exist
several approaches for guard sets defined by hyperplanes or polytopes, only few methods
are able to handle nonlinear guard sets. In this work we present a novel approach
to tightly enclose the intersections of reachable sets with nonlinear guard sets.
One major advantage of our method is its polynomial complexity with respect to the
system dimension, which makes it applicable for high-dimensional systems. Furthermore,
our approach can be combined with different reachability algorithms for continuous
systems due to its modular design. We demonstrate the advantages of our novel approach
compared to existing methods with numerical examples.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382200">Dynamics-aware subspace identification for decomposed aggregation in the reachability
analysis of hybrid automata</a></h3>
<ul class="DLauthors">
<li class="nameList">Viktorio S. el Hakim</li>
<li class="nameList Last">Marco J. G. Bekooij</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABNyZfMnJbc6vy3HdpeAhV0a/Viktorio%20El%20Hakim%20-%20HSCC_Slides_33.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABuOhiafE4Lne7uMGCLej86a/Viktorio%20El%20Hakim%20-%20HSCC_Video_33.mp4?dl=0">video</a>
<div style="display:inline">
<p>Hybrid automata are an emerging formalism used to model sampled-data control Cyber-Physical
Systems (CPS), and analyze their behavior using reachability analysis. This is because
hybrid automata provide a richer and more flexible modeling framework, compared to
traditional approaches. However, modern state-of-the-art tools struggle to analyze
such systems, due to the computational complexity of the reachability algorithm, and
due to the introduced overapproximation error. These shortcomings are largely attributed
(but not limited) to the aggregation of sets.
</p>
<p>In this paper we propose a subspace identification approach for decomposed aggregation
in the reachability analysis of hybrid automata with linear dynamics. Our key contribution
is the observation that the choice of a good subspace basis does not only depend on
the sets being aggregated, but also on the continuous-time dynamics of an automaton.
With this observation in mind, we present a dynamics-aware sub-space identification
algorithm that we use to construct tight decomposed convex hulls for the aggregated
sets.
</p>
<p>Our approach is evaluated on two practically relevant hybrid automata models of sampled-data
CPS that have been shown to be difficult to analyze by modern state-of-the-art tools.
Specifically, we show that for these models our approach can improve the accuracy
of the reachable set by up-to 10 times when compared to standard Principal Component
Analysis (PCA), for which finding a fixed point is not guaranteed. We also show that
while the computational complexity is increased, a fixed-point is found earlier.
</p>
</div>
</details>
<h2>SESSION: Linear system analysis</h2>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382195">Worst-case topological entropy and minimal data rate for state observation of switched
linear systems</a></h3>
<ul class="DLauthors">
<li class="nameList">Guillaume O. Berger</li>
<li class="nameList Last">Raphaël M. Jungers</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAD-zPB2bbWvy1l18GDf9kSaa/Guillaume%20Berger%20-%20HSCC_Slides_12.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADEr972vrWfaJhrhDIQ3Te_a/Guillaume%20Berger%20-%20HSCC_Video_12.mp4?dl=0">video</a>
<div style="display:inline">
<p>We introduce and study the concept of worst-case topological entropy of switched linear
systems under arbitrary switching. It is shown that this quantity is equal to the
minimal data rate (number of bits per second) required for the state observation of
the switched linear system with any switching signal. A computable closed-form expression
is presented for the worst-case topological entropy of switched linear systems. Finally,
a practical coder-decoder, operating at a data rate arbitrarily close to the worst-case
topological entropy, is described.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382204">Piece-wise analytic trajectory computation for polytopic switching between stable
affine systems</a></h3>
<ul class="DLauthors">
<li class="nameList Last">Maben Rabi</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADNzLaBLYD0mZFrNtmCGhFta/Maben%20Rabi%20-%20HSCC_Slides_42.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADSlfGyZ8VI3s_EDnnLBt8Ba/Maben%20Rabi%20-%20HSCC_Video_42.mp4?dl=0">video</a>
<div style="display:inline">
<p>Our problem is to compute trajectories of a hybrid system that switches between stable
affine ODEs, with switching triggered by hyperplane crossings. Instead of integrating
over relatively short time steps, we propose to analytically calculate the affine
ODE trajectories between switching times. Our algorithm computes the switching times
themselves by Chebyshev interpolation of the analytic trajectory pieces, and polynomial
root finding. We shrink the interpolation time intervals using bounds on the times
needed by the affine ODE trajectories to enter certain Lyapunov sub-level sets. Based
on the Chebfun package, we give a MATLAB implementation of our algorithm. We find
that this implementation simulates Relay feedback systems as accurately and sometimes
faster than conventional algorithms.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382213">AReN: assured ReLU NN architecture for model predictive control of LTI systems</a></h3>
<ul class="DLauthors">
<li class="nameList">James Ferlez</li>
<li class="nameList Last">Yasser Shoukry</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://berkeley.box.com/s/p213mvhgtnp9d7tfu6wsrd6kq2v7xg5u">video</a>
<div style="display:inline">
<p>In this paper, we consider the problem of automatically designing a Rectified Linear
Unit (ReLU) Neural Network (NN) architecture that is sufficient to implement the optimal
Model Predictive Control (MPC) strategy for an LTI system with quadratic cost. Specifically,
we propose AReN, an algorithm to generate Assured ReLU Architectures. AReN takes as
input an LTI system with quadratic cost specification, and outputs a ReLU NN architecture
with the assurance that there exist network weights that exactly implement the associated
MPC controller. AReN thus offers new insight into the design of ReLU NN architectures
for the control of LTI systems: instead of training a heuristically chosen NN architecture
on data - or iterating over many architectures until a suitable one is found - AReN
can suggest an adequate NN architecture before training begins. While several previous
works were inspired by the fact that ReLU NN controllers and optimal MPC controllers
are both Continuous, Piecewise-Linear (CPWL) functions, exploiting this similarity
to design NN architectures with correctness guarantees has remained elusive. AReN
achieves this using two novel features. First, we reinterpret a recent result about
the implementation of CPWL functions via ReLU NNs to show that a CPWL function may
be implemented by a ReLU architecture that is determined by the number of distinct
affine regions in the function. Second, we show that we can efficiently over-approximate
the number of affine regions in the optimal MPC controller without solving the MPC
problem exactly. Together, these results connect the MPC problem to a ReLU NN implementation
without explicitly solving the MPC: the result is a NN architecture that has the assurance
that it can implement the MPC controller. We show through numerical results the effectiveness
of AReN in designing an NN architecture.
</p>
</div>
</details>
<h2>SESSION: Temporal logic</h2>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382197">From LTL to rLTL monitoring: improved monitorability through robust semantics</a></h3>
<ul class="DLauthors">
<li class="nameList">Corto Mascle</li>
<li class="nameList">Daniel Neider</li>
<li class="nameList">Maximilian Schwenger</li>
<li class="nameList">Paulo Tabuada</li>
<li class="nameList">Alexander Weinert</li>
<li class="nameList Last">Martin Zimmermann</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<div style="display:inline">
<p>Runtime monitoring is commonly used to detect the violation of desired properties
in safety critical cyber-physical systems by observing its executions. Bauer et al.
introduced an influential framework for monitoring Linear Temporal Logic (LTL) properties
based on a three-valued semantics: the formula is already satisfied by the given prefix,
it is already violated, or it is still undetermined, i.e., it can still be satisfied
and violated by appropriate extensions. However, a wide range of formulas are not
monitorable under this approach, meaning that they have a prefix for which satisfaction
and violation will always remain undetermined no matter how it is extended. In particular,
Bauer et al. report that 44% of the formulas they consider in their experiments fall
into this category.
</p>
<p>Recently, a robust semantics for LTL was introduced to capture different degrees by
wich a property can be violated. In this paper we introduce a robust semantics for
finite strings and show its potential in monitoring: every formula considered by Bauer
et al. is monitorable under our approach. Furthermore, we discuss which properties
that come naturally in LTL monitoring --- such as the realizability of all truth values
--- can be transferred to the robust setting. Lastly, we show that LTL formulas with
robust semantics can be monitored by deterministic automata and report on a prototype
implementation.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382223">Sufficient conditions for satisfaction of formulas with until operators in hybrid
systems</a></h3>
<ul class="DLauthors">
<li class="nameList">Hyejin Han</li>
<li class="nameList">Mohamed Maghenem</li>
<li class="nameList Last">Ricardo G. Sanfelice</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAB9lMU_D-z3nhZz27mT_NLOa/Hyejin%20Han%20-%20HSCC_Slides_74.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAD1I8neM5MyUXha4GWIIpOma/Hyejin%20Han%20-%20HSCC_Video_74.mp4?dl=0">video</a>
<div style="display:inline">
<p>In this paper, we introduce tools to verify the satisfaction of temporal logic specifications
using the until operator for hybrid dynamical systems. Hybrid dynamical systems are
given in terms of differential and difference inclusions, which capture the continuous
and discrete dynamics (or events), respectively. For such systems, conditional invariance
and eventual conditional invariance are employed to characterize dynamical properties
associated with the until operators. Sufficient conditions for the satisfaction of
temporal logic specifications involving the until operator are provided by guaranteeing
properties of the data defining the systems and the existence of barrier functions
or Lyapunov-like functions. Examples illustrate the results throughout the paper.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382218">Interpretable classification of time-series data using efficient enumerative techniques</a></h3>
<ul class="DLauthors">
<li class="nameList">Sara Mohammadinejad</li>
<li class="nameList">Jyotirmoy V. Deshmukh</li>
<li class="nameList">Aniruddh G. Puranic</li>
<li class="nameList">Marcell Vazquez-Chanlatte</li>
<li class="nameList Last">Alexandre Donzé</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAB9jw6vDwi54lJCHbwxkKzia/Sara%20Mohammadinejad%20-%20HSCC_Slides_64.pptx?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAdgc0BOIlED7AC7GSEnGzsa/Sara%20Mohammadinejad%20-%20HSCC_Video_64.mov?dl=0">video</a>
<div style="display:inline">
<p>Cyber-physical system applications such as autonomous vehicles, wearable devices,
and avionic systems generate a large volume of time-series data. Designers often look
for tools to help classify and categorize the data. Traditional machine learning techniques
for time-series data offer several solutions to solve these problems; however, the
artifacts trained by these algorithms often lack interpretability. On the other hand,
temporal logic, such as Signal Temporal Logic (STL) have been successfully used in
the formal methods community as specifications of time-series behaviors. In this work,
we propose a new technique to automatically learn temporal logic formulas that are
able to classify real-valued time-series data. Previous work on learning STL formulas
from data either assumes a formula-template to be given by the user, or assumes some
special fragment of STL that enables exploring the formula structure in a systematic
fashion. In our technique, we relax these assumptions, and provide a way to systematically
explore the space of all STL formulas. As the space of all STL formulas is very large,
and contains many semantically equivalent formulas, we suggest a technique to heuristically
prune the space of formulas considered. Finally, we illustrate our technique on various
case studies from the automotive and transportation domains.
</p>
</div>
</details>
<h2>SESSION: Formal verification</h2>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382198">Classic and non-prophetic model checking for hybrid Petri nets with stochastic firings</a></h3>
<ul class="DLauthors">
<li class="nameList">Carina Pilch</li>
<li class="nameList">Arnd Hartmanns</li>
<li class="nameList Last">Anne Remke</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADmwIu7uJn6u79ztWE8RI2Da/Carina%20Pilch%20-%20HSCC_Slides_29.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADq6T9CWXPnft9EXjRJzM_Va/Carina%20Pilch%20-%20HSCC_Video_29.mp4?dl=0">video</a>
<div style="display:inline">
<p>Nondeterminism occurs naturally in Petri nets whenever multiple events are enabled
at the same time. Traditionally, it is resolved at specification time using probability
weights and priorities. In this paper, we focus on model checking for hybrid Petri
nets with an arbitrary but finite number of stochastic firings (HPnGs) while preserving
the inherent nondeterminism as a first-class modelling and analysis feature. We present
two algorithms to compute optimal <em>non-prophetic</em> and <em>prophetic</em> schedulers. The former can be applied to all HPnG models while the latter is only
applicable if information on the firing times of general transitions is specifically
encoded in the model. Both algorithms make use of recent work on the parametric location
tree, which symbolically unfolds the state space of an HPnG. A running example illustrates
the approach and confirms the feasibility of the presented algorithm.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382193">Falsification of cyber-physical systems with robustness-guided black-box checking</a></h3>
<ul class="DLauthors">
<li class="nameList Last">Masaki Waga</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADRKvponQ0gH7C3jUjTU5J2a/Masaki%20Waga%20-%20HSCC_Slides_10.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABP_4obuvrAnJs3GhyaTCQma/Masaki%20Waga%20-%20HSCC_Video_10.mp4?dl=0">video</a>
<div style="display:inline">
<p>For exhaustive formal verification, industrial-scale <em>cyber-physical systems (CPSs)</em> are often too large and complex, and lightweight alternatives (e.g., monitoring and
testing) have attracted the attention of both industrial practitioners and academic
researchers. <em>Falsification</em> is one popular testing method of CPSs utilizing <em>stochastic optimization.</em> In state-of-the-art falsification methods, the result of the previous falsification
trials is discarded, and we always try to falsify without any prior knowledge. To
concisely memorize such prior information on the CPS model and exploit it, we employ
<em>Black-box checking (BBC)</em>, which is a combination of <em>automata learning</em> and <em>model checking.</em> Moreover, we enhance BBC using the <em>robust semantics</em> of STL formulas, which is the essential gadget in falsification. Our experiment results
suggest that our robustness-guided BBC outperforms a state-of-the-art falsification
tool.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382209">Statistical verification of learning-based cyber-physical systems</a></h3>
<ul class="DLauthors">
<li class="nameList">Mojtaba Zarei</li>
<li class="nameList">Yu Wang</li>
<li class="nameList Last">Miroslav Pajic</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABtZ1DK-EK0c_mfME_dbXHya/Yu%20Wang%20-%20HSCC_Slides_48.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAriiaGCq6mfx1jJWbb49MYa/Yu%20Wang%20-%20HSCC_Video_48.mp4?dl=0">video</a>
<div style="display:inline">
<p>The use of Neural Network (NN)-based controllers has attracted significant attention
in recent years. Yet, due to the complexity and non-linearity of such NN-based cyber-physical
systems (CPS), existing verification techniques that employ exhaustive state-space
search, face significant scalability challenges; this effectively limits their use
for analysis of real-world CPS. In this work, we focus on the use of Statistical Model
Checking (SMC) for verifying complex NN-controlled CPS. Using an SMC approach based
on Clopper-Pearson confidence levels, we verify from samples specifications that are
captured by Signal Temporal Logic (STL) formulas. Specifically, we consider three
CPS benchmarks with varying levels of plant and controller complexity, as well as
the type of considered STL properties - reachability property for a mountain car,
safety property for a bipedal robot, and control performance of the closed-loop magnet
levitation system. On these benchmarks, we show that SMC methods can be successfully
used to provide high-assurance for learning-based CPS.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382210">Conformance verification for neural network models of glucose-insulin dynamics</a></h3>
<ul class="DLauthors">
<li class="nameList">Taisa Kushner</li>
<li class="nameList">Sriram Sankaranarayanan</li>
<li class="nameList Last">Marc Breton</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://berkeley.box.com/s/hq6br4ynsxdmftklor314ybd4ymgzct4">video</a>
<div style="display:inline">
<p>Neural networks present a useful framework for learning complex dynamics, and are
increasingly being considered as components to closed loop predictive control algorithms.
However, if they are to be utilized in such safety-critical advisory settings, they
must be provably "conformant" to the governing scientific (biological, chemical, physical)
laws which underlie the modeled process. Unfortunately, this is not easily guaranteed
as neural network models are prone to learn patterns which are artifacts of the conditions
under which the training data is collected, which may not necessarily conform to underlying
physiological laws.
</p>
<p>In this work, we utilize a formal range-propagation based approach for checking whether
neural network models for predicting future blood glucose levels of individuals with
type-1 diabetes are monotonic in terms of their insulin inputs. These networks are
increasingly part of closed loop predictive control algorithms for "artificial pancreas"
devices which automate control of insulin delivery for individuals with type-1 diabetes.
Our approach considers a key property that blood glucose levels must be monotonically
decreasing with increasing insulin inputs to the model. Multiple representative neural
network models for blood glucose prediction are trained and tested on real patient
data, and conformance is tested through our verification approach. We observe that
standard approaches to training networks result in models which violate the core relationship
between insulin inputs and glucose levels, despite having high prediction accuracy.
We propose an approach that can learn conformant models without much loss in accuracy.
</p>
</div>
</details>
<h2>SESSION: Formal synthesis</h2>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382214">Symbolic controller synthesis for Büchi specifications on stochastic systems</a></h3>
<ul class="DLauthors">
<li class="nameList">Rupak Majumdar</li>
<li class="nameList">Kaushik Mallik</li>
<li class="nameList Last">Sadegh Soudjani</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABiYslFUaZgmDrUnt5swbpYa/Kaushik%20Mallik%20-%20HSCC_Slides_58.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADQyR12HvlKUGvrQpmWnA63a/Kaushik%20Mallik%20-%20HSCC_Video_58.m4v?dl=0">video</a>
<div style="display:inline">
<p>We consider the policy synthesis problem for continuous-state controlled Markov processes
evolving in discrete time, when the specification is given as a Büchi condition (visit
a set of states infinitely often). We decompose computation of the maximal probability
of satisfying the Büchi condition into two steps. The first step is to compute the
maximal <em>qualitative winning set</em>, from where the Büchi condition can be enforced with probability one. The second
step is to find the maximal probability of reaching the already computed qualitative
winning set. In contrast with finite-state models, we show that such a computation
only gives a lower bound on the maximal probability where the gap can be non-zero.
</p>
<p>In this paper we focus on approximating the qualitative winning set, while pointing
out that the existing approaches for unbounded reachability computation can solve
the second step. We provide an abstraction-based technique to approximate the qualitative
winning set by simultaneously using an over- and under-approximation of the probabilistic
transition relation. Since we are interested in qualitative properties, the abstraction
is non-probabilistic; instead, the probabilistic transitions are assumed to be under
the control of a (fair) adversary. Thus, we reduce the original policy synthesis problem
to a Büchi game under a fairness assumption and characterize upper and lower bounds
on winning sets as nested fixed point expressions in the μ-calculus. This characterization
immediately provides a symbolic algorithm scheme. Further, a winning strategy computed
on the abstract game can be refined to a policy on the controlled Markov process.
</p>
<p>We describe a concrete abstraction procedure and demonstrate our algorithm on two
case studies. We show that our techniques are able to provide tight approximations
to the qualitative winning set for the Van der Pol oscillator and a 3-d Dubins' vehicle.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382219">On abstraction-based controller design with output feedback</a></h3>
<ul class="DLauthors">
<li class="nameList">Rupak Majumdar</li>
<li class="nameList">Necmiye Ozay</li>
<li class="nameList Last">Anne-Kathrin Schmuck</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AACkDF6mddIX-GkcYIZcvvBsa/Necmiye%20Ozay%20-%20HSCC_Slides_67.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AACi0NMmLR32u8Gs3jSsYFbYa/Necmiye%20Ozay%20-%20HSCC_Video_67.mkv?dl=0">video</a>
<div style="display:inline">
<p>We consider abstraction-based design of <em>output-feedback</em> controllers for dynamical systems with a finite set of inputs and outputs against
specifications in linear-time temporal logic. The usual procedure for abstraction-based
controller design (ABCD) first constructs a <em>finite-state</em> abstraction of the underlying dynamical system, and second, uses reactive synthesis
techniques to compute an abstract <em>state-feedback</em> controller on the abstraction. In this context, our contribution is two-fold: (I)
we define a suitable relation between the original system and its abstraction which
characterizes the soundness and completeness conditions for an abstract <em>state-feedback</em> controller to be refined to a concrete <em>output-feedback</em> controller for the original system, and (II) we provide an algorithm to compute a
<em>sound finite-state abstraction</em> fulfilling this relation.
</p>
<p>Our relation generalizes feedback-refinement relations from ABCD with state-feedback.
Our algorithm for constructing sound finite-state abstractions is inspired by the
simultaneous reachability and bisimulation minimization algorithm of Lee and Yannakakis.
We lift their idea to the computation of an observation-equivalent system and show
how <em>sound abstractions</em> can be obtained by stopping this algorithm at any point. Additionally, our new algorithm
produces a realization of the topological closure of the input/output behavior of
the original system if it is finite-state realizable.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382212">Compositional synthesis via a convex parameterization of assume-guarantee contracts</a></h3>
<ul class="DLauthors">
<li class="nameList">Kasra Ghasemi</li>
<li class="nameList">Sadra Sadraddini</li>
<li class="nameList Last">Calin Belta</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAD3DT-L5r8iy90Laiy7UucUa/Kasra%20Ghasemi%20-%20HSCC_Slides_55.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AADr6CqNQo7U_7Pl_tEYLJeha/Kasra%20Ghasemi%20-%20HSCC_Video_55.mp4?dl=0">video</a>
<div style="display:inline">
<p>We develop an assume-guarantee framework for control of large scale linear (time-varying)
systems from finite-time reach and avoid or infinite-time invariance specifications.
The contracts describe the admissible set of states and controls for individual subsystems.
A set of contracts compose correctly if mutual assumptions and guarantees match in
a way that we formalize. We propose a rich parameterization of contracts such that
the set of parameters that compose correctly is convex. Moreover, we design a potential
function of parameters that describes the distance of contracts from a correct composition.
Thus, the verification and synthesis for the aggregate system are broken to solving
small convex programs for individual subsystems, where correctness is ultimately achieved
in a compositional way. Illustrative examples demonstrate the scalability of our method.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382220">dtControl: decision tree learning algorithms for controller representation</a></h3>
<ul class="DLauthors">
<li class="nameList">Pranav Ashok</li>
<li class="nameList">Mathias Jackermeier</li>
<li class="nameList">Pushpak Jagtap</li>
<li class="nameList">Jan Křetínský</li>
<li class="nameList">Maximilian Weininger</li>
<li class="nameList Last">Majid Zamani</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAD23bMYAJB158sqZRggGEmCa/Pranav%20Ashok%20-%20HSCC_Slides_69.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAxMr7cFxcZ_BDfHgQDn8kEa/Pranav%20Ashok%20-%20HSCC_Video_69.mp4?dl=0">video</a>
<div style="display:inline">
<p>Decision tree learning is a popular classification technique most commonly used in
machine learning applications. Recent work has shown that decision trees can be used
to represent provably-correct controllers concisely. Compared to representations using
lookup tables or binary decision diagrams, decision trees are smaller and more explainable.
We present dtControl, an easily extensible tool for representing memoryless controllers
as decision trees. We give a comprehensive evaluation of various decision tree learning
algorithms applied to 10 case studies arising out of correct-by-construction controller
synthesis. These algorithms include two new techniques, one for using arbitrary linear
binary classifiers in the decision tree learning, and one novel approach for determinizing
controllers during the decision tree construction. In particular the latter turns
out to be extremely efficient, yielding decision trees with a single-digit number
of decision nodes on 5 of the case studies.
</p>
</div>
</details>
<h2>SESSION: Hybrid systems theory</h2>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382202">A computable and compositional semantics for hybrid automata</a></h3>
<ul class="DLauthors">
<li class="nameList">Davide Bresolin</li>
<li class="nameList">Pieter Collins</li>
<li class="nameList">Luca Geretti</li>
<li class="nameList">Roberto Segala</li>
<li class="nameList">Tiziano Villa</li>
<li class="nameList Last">Sanja Živanović Gonzalez</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAANFP3v2pt18xCKGNiBZOB-a/Davide%20Bresolin%20-%20HSCC_Slides_39.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAA2-FikV4WhwUGYkUVm1E-ta/Davide%20Bresolin%20-%20HSCC_Video_39.mp4?dl=0">video</a>
<div style="display:inline">
<p>Hybrid Systems are systems having a mixed discrete and continuous behaviour that cannot
be characterized faithfully using either only discrete or only continuous models.
A good framework for hybrid systems should support their <em>compositional</em> description and analysis, since commonly systems are specified by a composition of
smaller subsystems, to cope with the complexity of their monolithic representation.
Moreover, since the reachability problem for hybrid systems is undecidable, one should
investigate the conditions that guarantee <em>approximate</em> computability of composition, when only approximations to the exact problem data
are available.
</p>
<p>In this paper, we propose an automata-based formalism (HIOA) for hybrid systems that
is compositional and for which the evolution can be computed approximately. The main
results are that the composition of compatible HIOA yields a pre-HIOA; a dominance
result on the composition of HIOA by which we can replace any component in a composition
by another one that exhibits the same external behaviour without affecting the behaviour
of the composition; finally, the key result that the composition of two compatible
upper(lower)-semicontinuous HIOA is a computable upper(lower)-semicontinuous pre-HIOA,
which entails that the evolution of the composition is upper(lower)-semicomputable.
A discussion on how compositionality/computability are handled in state-of-art libraries
for reachability analysis closes the paper.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382221">Does sample-time emulation preserve exponential stability?</a></h3>
<ul class="DLauthors">
<li class="nameList Last">Anton V. Proskurnikov</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAD4HuPQJsX3PCT6u6k6NZ8Da/Anton%20Proskurnikov%20-%20proskurnikov_emulation.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAf547eXlaay20ZXQC_cW8ua/Anton%20Proskurnikov%20-%20proskurnikov_emulation.mp4?dl=0">video</a>
<div style="display:inline">
<p>Whereas classical control theory provides many methods for designing continuous-time
feedback controllers, nowadays control algorithms are implemented on digital platforms
and have to be designed in sampled time. Approaches to sampled-time control design
are based on either discretization of the plant enabling discrete-time controller
synthesis, or various redesign methods converting a continuous-time controller into
a sampled-time approximation, providing comparable closed-loop system properties.
The simplest of redesign approaches, typically used in practice, is the <em>emulation</em> of continuous-time feedback by sufficiently fast sampling. In spite of its simplicity,
emulation gives rise to an important problem: does emulation at a sufficiently high
rate (or, equivalently, with a small sampling time) preserve the stability of the
closed-loop system? In this paper, we address this problem for the case of <em>exponential</em> stability (local or global). Even for linear systems, the problem of stability preservation
becomes non-trivial when sampling is aperiodic. For nonlinear systems, viability of
emulation approach is usually proved only under quite restrictive assumptions on the
plant and the controller, which, as will be shown, in fact be discarded.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382201">Implicit structural analysis of multimode DAE systems</a></h3>
<ul class="DLauthors">
<li class="nameList">Benoît Caillaud</li>
<li class="nameList">Mathias Malandain</li>
<li class="nameList Last">Joan Thibault</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AACY33hAwJP3ftPguW2WGlI-a/Benoit%20Caillaud%20-%20HSCC_Slides_35.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAd1m522hc61rozmWPRI2tfa/Benoit%20Caillaud%20-%20HSCC_Video_35.mp4?dl=0">video</a>
<div style="display:inline">
<p>Modeling languages and tools based on Differential Algebraic Equations (DAE) bring
several specific issues that do not exist with modeling languages based on Ordinary
Differential Equations. The main problem is the determination of the differentiation
index and latent equations. Prior to generating simulation code and calling solvers,
the compilation of a model requires a structural analysis step, which reduces the
differentiation index to a level acceptable by numerical solvers.
</p>
<p>The Modelica language, among others, allows hybrid models with multiple modes, mode-dependent
dynamics and state-dependent mode switching. These Multimode DAE (mDAE) systems are
much harder to deal with. The main difficulties are (i) the combinatorial explosion
of the number of modes, and (ii) the correct handling of mode switchings.
</p>
<p>The focus of this paper is on the first issue, namely: How can one perform a structural
analysis of an mDAE in all possible modes, without enumerating these modes? A structural
analysis algorithm for mDAE systems is presented, based on an implicit representation
of their varying structure. It generalizes J. Pryce's Σ-method to the multimode case
and uses Binary Decision Diagrams (BDD) to represent the mode-dependent structure
of an mDAE. The algorithm determines, as a function of the mode, the set of latent
equations, the leading variables and the state vector. This is then used to compute
a conditional block dependency graph of the system, that can be used to generate efficient
simulation code with a mode-dependent scheduling of the blocks of equations.
</p>
</div>
</details>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382215">Local lipschitzness of reachability maps for hybrid systems with applications to safety</a></h3>
<ul class="DLauthors">
<li class="nameList">Mohamed Maghenem</li>
<li class="nameList Last">Ricardo G. Sanfelice</li>
</ul>
<details open=true class="DLabstract">
<summary>Details (abstract, video, slides)</summary>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AAAG2pDFDE91HPFgA1ZbAzqca/Mohamed%20MAGHENEM%20-%20HSCC_Slides_61.pdf?dl=0">slides</a>
<a href="https://www.dropbox.com/sh/yg9jo7splev779g/AABgSHSPILw55sCyP44TWbDya/Mohamed%20MAGHENEM%20-%20HSCC_Video_61.mp4?dl=0">video</a>
<div style="display:inline">
<p>Motivated by the safety problem, several definitions of reachability maps, for hybrid
dynamical systems, are introduced. It is well established that, under certain conditions,
the solutions to continuous-time systems depend continuously with respect to initial
conditions. In such setting, the reachability maps considered in this paper are locally
Lipschitz (in the Lipschitz sense for set-valued maps) when the right-hand side of
the continuous-time system is locally Lipschitz. However, guaranteeing similar properties
for reachability maps for hybrid systems is much more challenging. Examples of hybrid
systems for which the reachability maps do not depend nicely with respect to their
arguments, in the Lipschitz sense, are introduced. With such pathological cases properly
identified, sufficient conditions involving the data defining a hybrid system assuring
Lipschitzness of the reachability maps are formulated. As an application, the proposed
conditions are shown to be useful to significantly improve an existing converse theorem
for safety given in terms of barrier functions. Namely, for a class of safe hybrid
systems, we show that safety is equivalent to the existence of a locally Lipschitz
barrier function. Examples throughout the paper illustrate the results.
</p>
</div>
</details>
<h2>SESSION: Safety-critical control</h2>
<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" href="https://dl.acm.org/doi/abs/10.1145/3365365.3382196">Compositional construction of control barrier functions for interconnected control
systems</a></h3>
<ul class="DLauthors">
<li class="nameList">Pushpak Jagtap</li>