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Disruptive Innovations and Disruptive Assurance: Assuring Machine Learning and Autonomy
Autonomous and machine learning-based systems are disruptive innovations and thus require a corresponding disruptive assurance strategy. We offer an overview of a framework based on claims, arguments, and evidence aimed at addressing these systems and use it to identify specific gaps, challenges, and potential solutions
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Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 2
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Practical issues for the implementation of survivability and recovery techniques in optical networks
Promises, Impositions, and other Directionals
Promises, impositions, proposals, predictions, and suggestions are
categorized as voluntary co-operational methods. The class of voluntary
co-operational methods is included in the class of so-called directionals.
Directionals are mechanisms supporting the mutual coordination of autonomous
agents.
Notations are provided capable of expressing residual fragments of
directionals. An extensive example, involving promises about the suitability of
programs for tasks imposed on the promisee is presented. The example
illustrates the dynamics of promises and more specifically the corresponding
mechanism of trust updating and credibility updating. Trust levels and
credibility levels then determine the way certain promises and impositions are
handled.
The ubiquity of promises and impositions is further demonstrated with two
extensive examples involving human behaviour: an artificial example about an
agent planning a purchase, and a realistic example describing technology
mediated interaction concerning the solution of pay station failure related
problems arising for an agent intending to leave the parking area.Comment: 55 page
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