Symbolic logic framework for situational awareness in mixed autonomy

SymAware addresses the fundamental need for a new conceptual framework for awareness in multi-agent systems (MASs) that is compatible with the internal models and specifications of robotic agents and that enables safe simultaneous operation of collaborating autonomous agents and humans.

The goal of SymAware is to provide a comprehensive framework for situational awareness to support sustainable autonomy via agents that actively perceive risks and collaborate with other robots and humans to improve their awareness and understanding, while fulfilling complex and dynamically changing tasks.The SymAware framework will use compositional logic, symbolic computations, formal reasoning, and uncertainty quantification to characterise and support situational awareness of MAS in its various dimensions, sustaining awareness by learning in social contexts, quantifying risks based on limited knowledge, and formulating risk-aware negotiation of task distributions.

These objectives will be achieved in SymAware through (a) logical characterisation of awareness using symbolic methods, (b) quantifying the symbolic reasoning for awareness with spatial and temporal ingredients for decision making, (c) risk awareness via quantified knowledge, (d) quantifying and communicating knowledge awareness, (e) demonstrating awareness engineering in aviation and automotive use cases, and (f) identifying requirements for ethical and trustworthy awareness in human-agent interaction.The objectives of SymAware address the ""Awareness Inside"" Challenge of EIC by extending and formalising human-based models of situational awareness and by providing a novel conceptual situational awareness framework for MASs that encompasses logical characterisation and integrative formal reasoning of interdependent awareness dimensions including knowledge, spatiotemporal, risk and social dimensions.

This will support transitioning to safe mixed operation of autonomous agents and humans.