[agents] CFP: AIJ Special Issue on Risk-aware Autonomous Systems: Theory and Practice

[Bernardini, Sara]

Dear all,

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. Sara Bernardini (Royal Holloway University of London), Prof. Luca Carlone (MIT), Dr. Ashkan Jasour (MIT), Prof. Andreas Krause (ETH Zurich), Prof. George Pappas (University of Pennsylvania), Prof. Brian Williams (MIT) and Prof. Yisong Yue (Caltech). Submissions will close on October 15th, 2021 and the publication of the special issue is planned for August 15th, 2022. Artificial Intelligence is a world-leading journal in AI with an Impact Factor  of 6,628 and CiteScore of 7.7.

Aims and Scope

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.

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.

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.

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:

● risk-aware task and motion planning;
● robust and adversarial learning;
● certifiable and risk-aware perception, localization and mapping;
● robust task monitoring and execution under uncertainty;
● formal methods for monitoring and verifying uncertain systems;
● constraint and mathematical programming with chance constraints;
● robust control of intelligent systems;
● system-level monitoring and risk quantification.

Submission Instructions

We welcome high quality original (unpublished) articles. Each submission will be peer-reviewed.

All submissions should be formatted following the AI journal instructions for authors (https://www.elsevier.com/journals/artificial-intelligence/0004-3702/guide-for-authors) and submitted to: https://www.editorialmanager.com/artint/default.aspx

Important Dates

● Submissions open: 15 May 2021

● Submissions close: 15 October 2021

● Publication of the special issue: 15 August 2022

Guest Editors

● Prof. Sara Bernardini (Royal Holloway University of London, sara.bernardini at rhul.ac.uk<mailto:sara.bernardini at rhul.ac.uk>)
● Prof. Luca Carlone (Massachusetts Institute of Technology, lcarlone at mit.edu<mailto:lcarlone at mit.edu>)
● Dr. Ashkan Jasour (Massachusetts Institute of Technology, jasour at mit.edu<mailto:jasour at mit.edu>)
● Prof. Andreas Krause (ETH Zurich, krausea at ethz.ch<mailto:krausea at ethz.ch>)
● Prof. George Pappas (University of Pennsylvania, pappasg at seas.upenn.edu<mailto:pappasg at seas.upenn.edu>)
● Prof Brian Williams (Massachusetts Institute of Technology, williams at mit.edu<mailto:williams at mit.edu>)
● Prof. Yisong Yue (California Institute of Technology, yyue at caltech.edu<mailto:yyue at caltech.edu>)

For more information, please visit: https://www.journals.elsevier.com/artificial-intelligence/call-for-papers/risk-aware-autonomous-systems-theory-and-practice

All the best,

Sara

----
Sara Bernardini
Professor of Artificial Intelligence
Director of the MSc in Artificial Intelligence
Department of Computer Science
Royal Holloway University of London

Office: Bedford 2-24
Tel.: +44 1784 276792
Web: www.sara-bernardini.com<http://www.sara-bernardini.com>


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