Enabling perception for plan recognition in multi-agent air mission simulations

The potential synergy between instance-based pattern recognition and means-end (possible world) reasoning is explored for supporting plan recognition in multi-aeroplane air-mission simulations. A means-end-reasoning model is then used to deliberate about and invoke standard operating procedures, based on recognised activity. The reasoning model constrains the recognition process by framing queries according to what a pilot would expect during the execution of the current plant(s). The importance of capturing relative information in these multi-agent simulations is emphasised, including self-aeroplane, aeroplane-aeroplane and aeroplane-environment relationships