<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
</head>
<body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
Dear all,
<div class=""><br class="">
</div>
<div class="">Artificial Intelligence’s Special Issue on "Risk-aware Autonomous Systems: Theory and Practice" is now open for submissions. The special issue is co-edited by Prof. <b class="">Sara Bernardini
</b>(Royal Holloway University of London), Prof. <b class="">Luca Carlone </b>(MIT), Dr.
<b class="">Ashkan Jasour</b> (MIT), Prof. <b class="">Andreas Krause</b> (ETH Zurich), Prof.
<b class="">George Pappas</b> (University of Pennsylvania), Prof. <b class="">Brian Williams</b> (MIT) and Prof.
<b class="">Yisong Yue</b> (Caltech). Submissions will close on <b class="">October 15th, 2021</b> and the publication of the special issue is planned for <b class="">August 15th, 2022</b>. Artificial Intelligence is a world-leading journal in AI with an Impact
Factor of 6,628 and CiteScore of 7.7. </div>
<div class=""><br class="">
</div>
<div class=""><b class="">Aims and Scope</b><br class="">
<br class="">
This special issue focuses on the theory and practice of risk-aware autonomous systems that reason about uncertainty and risk online to achieve safety, and that combine machine learning and decision making to accomplish real world tasks.<br class="">
<br class="">
The topic of risk-aware autonomous systems has seen a dramatic increase in importance over the last few years, as autonomous systems are being deployed almost daily within safety-critical applications, including self-driving vehicles, autonomous undersea and
aerospace systems, service robotics, and collaborative manufacturing. This broad adoption is a testament to the fast-paced progress of the research community across multiple areas, including planning, learning, perception, decision making, and control. At
the same time, today’s widely used AI algorithms for autonomy are beginning to showcase fundamental limits and practical shortcomings. In particular, excessive risk taken by these algorithms can lead to catastrophic failure of the overall system and may put
human life in danger. Many AI methods used today do not attempt to quantify uncertainty; they do not assess the risks that uncertainty imposes on system safety and success; they do not guarantee bounds on this risk and they do not perform these assessments
in real-time.<br class="">
<br class="">
To push the envelope of autonomous systems’ safety, this special issue will present ground-breaking research on the theory and practice of designing the next generation of risk-aware AI algorithms and autonomous systems. Key to our envisioned methods is their
ability to account for uncertainty and risk of failure during their online execution, their capabilities for proactively quantifying and mitigating risks against task goals and safety constraints, and their ability to offer formal guarantees, such as bounds
on the risk of failure. Emerging risk-bounded methods often operate on models of uncertainty, specifications of intended outcomes, and specifications of acceptable risks regarding these outcomes. These models and specifications are diverse. Uncertainty models
may be probabilistic, set bounded, or interval based. Intended outcomes include goals achieved, deadlines met, safety constraints respected, required accuracy in model estimation and perception, and rate of false positives. Specifications of acceptable risk
include risk bounds and acceptable costs of failure. These intended outcomes and acceptable risks can apply to individual AI components, such as policy and action learners, image classifiers and planners, and the aggregate systems as a whole.<br class="">
<br class="">
This special issue is intended to represent this diversity. It aims to cover a broad set of topics related to risk-aware autonomous systems, including but not limited to:<br class="">
<br class="">
● risk-aware task and motion planning;<br class="">
● robust and adversarial learning;<br class="">
● certifiable and risk-aware perception, localization and mapping;<br class="">
● robust task monitoring and execution under uncertainty;<br class="">
● formal methods for monitoring and verifying uncertain systems;<br class="">
● constraint and mathematical programming with chance constraints;<br class="">
● robust control of intelligent systems;<br class="">
● system-level monitoring and risk quantification.<br class="">
<br class="">
<b class="">Submission Instructions</b><br class="">
<br class="">
We welcome high quality original (unpublished) articles. Each submission will be peer-reviewed.<br class="">
<br class="">
All submissions should be formatted following the AI journal instructions for authors (<a href="https://www.elsevier.com/journals/artificial-intelligence/0004-3702/guide-for-authors" class="">https://www.elsevier.com/journals/artificial-intelligence/0004-3702/guide-for-authors</a>)
and submitted to: <a href="https://www.editorialmanager.com/artint/default.aspx" class="">
https://www.editorialmanager.com/artint/default.aspx</a> </div>
<div class=""><br class="">
</div>
<div class=""><b class="">Important Dates</b><br class="">
<br class="">
● Submissions open: 15 May 2021<br class="">
<br class="">
● Submissions close: 15 October 2021<br class="">
<br class="">
● Publication of the special issue: 15 August 2022</div>
<div class=""><b class=""><br class="">
</b></div>
<div class=""><b class="">Guest Editors</b><br class="">
<br class="">
● Prof. Sara Bernardini (Royal Holloway University of London, <a href="mailto:sara.bernardini@rhul.ac.uk" class="">sara.bernardini@rhul.ac.uk</a>)<br class="">
● Prof. Luca Carlone (Massachusetts Institute of Technology, <a href="mailto:lcarlone@mit.edu" class="">lcarlone@mit.edu</a>)<br class="">
● Dr. Ashkan Jasour (Massachusetts Institute of Technology, <a href="mailto:jasour@mit.edu" class="">jasour@mit.edu</a>)<br class="">
● Prof. Andreas Krause (ETH Zurich, <a href="mailto:krausea@ethz.ch" class="">krausea@ethz.ch</a>)<br class="">
● Prof. George Pappas (University of Pennsylvania, <a href="mailto:pappasg@seas.upenn.edu" class="">pappasg@seas.upenn.edu</a>)<br class="">
● Prof Brian Williams (Massachusetts Institute of Technology, <a href="mailto:williams@mit.edu" class="">williams@mit.edu</a>)<br class="">
● Prof. Yisong Yue (California Institute of Technology, <a href="mailto:yyue@caltech.edu" class="">yyue@caltech.edu</a>)</div>
<div class=""><br class="">
</div>
<div class="">For more information, please visit: <a href="https://www.journals.elsevier.com/artificial-intelligence/call-for-papers/risk-aware-autonomous-systems-theory-and-practice" class="">
https://www.journals.elsevier.com/artificial-intelligence/call-for-papers/risk-aware-autonomous-systems-theory-and-practice</a></div>
<div class=""><br class="">
</div>
<div class="">All the best,</div>
<div class=""><br class="">
</div>
<div class="">Sara</div>
<div class=""><br class="">
<div class="">----<br class="">
Sara Bernardini<br class="">
Professor of Artificial Intelligence<br class="">
Director of the MSc in Artificial Intelligence<br class="">
Department of Computer Science <br class="">
Royal Holloway University of London<br class="">
<br class="">
Office: Bedford 2-24 <br class="">
Tel.: +44 1784 276792<br class="">
Web: <a href="http://www.sara-bernardini.com" class="">www.sara-bernardini.com</a></div>
</div>
<div class=""><br class="">
</div>
<p>This email, its contents and any attachments are intended solely for the addressee and may contain confidential information. In certain circumstances, it may also be subject to legal privilege. Any unauthorised use, disclosure, or copying is not permitted.
If you have received this email in error, please notify us and immediately and permanently delete it. Any views or opinions expressed in personal emails are solely those of the author and do not necessarily represent those of Royal Holloway, University of
London. It is your responsibility to ensure that this email and any attachments are virus free.</p>
<p></p>
</body>
</html>