SymAware: Symbolic Logic Framework for Situational Awareness in Mixed Autonomy
SymAware provides a novel conceptual framework for situational awareness in multi-agent systems that is compatible with the internal models and specifications of robot agents and that enables the safe simultaneous operation of collaborating autonomous agents and humans.
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 the 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. Positions are currently being advertised for this project and details can be found here.
SymAware: Symbolic Logic Framework for Situational Awareness in Mixed Autonomy
SymAware provides a novel conceptual framework for situational awareness in multi-agent systems that is compatible with the internal models and specifications of robot agents and that enables the safe simultaneous operation of collaborating autonomous agents and humans.
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 the 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. Positions are currently being advertised for this project and details can be found here.
