38,082 research outputs found
Improving AI systems' dependability by utilizing historical knowledge
A Turing Test is a promising way to validate AI systems which usually have no way to proof correctness. However, human experts (validators) are often too busy to participate in it and sometimes have different opinions per person as well as per validation session. To cope with these and increase the validation dependability, a Validation Knowledge Base (VKB) in Turing Test - like validation is proposed. The VKB is constructed and maintained across various validation sessions. Primary benefits are (1) decreasing validators' workload, (2) refining the methodology itself, e.g. selecting dependable validators using V KB, and (3) increasing AI systems' dependabilities through dependable validation, e.g. support to identify optimal solutions. Finally, Validation Experts Software Agents (VESA) are introduced to further break limitations of human validator's dependability. Each VESA is a software agent corresponding to a particular human validator. This suggests the ability to systematically "construct" human-like validators by keeping personal validation knowledge per corresponding validator. This will bring a new dimension towards dependable AI systems
Common Representation of Information Flows for Dynamic Coalitions
We propose a formal foundation for reasoning about access control policies
within a Dynamic Coalition, defining an abstraction over existing access
control models and providing mechanisms for translation of those models into
information-flow domain. The abstracted information-flow domain model, called a
Common Representation, can then be used for defining a way to control the
evolution of Dynamic Coalitions with respect to information flow
Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
The success of the human-robot co-worker team in a flexible manufacturing
environment where robots learn from demonstration heavily relies on the correct
and safe operation of the robot. How this can be achieved is a challenge that
requires addressing both technical as well as human-centric research questions.
In this paper we discuss the state of the art in safety assurance, existing as
well as emerging standards in this area, and the need for new approaches to
safety assurance in the context of learning machines. We then focus on robotic
learning from demonstration, the challenges these techniques pose to safety
assurance and indicate opportunities to integrate safety considerations into
algorithms "by design". Finally, from a human-centric perspective, we stipulate
that, to achieve high levels of safety and ultimately trust, the robotic
co-worker must meet the innate expectations of the humans it works with. It is
our aim to stimulate a discussion focused on the safety aspects of
human-in-the-loop robotics, and to foster multidisciplinary collaboration to
address the research challenges identified
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