We propose that "reverse engineering" human ToM by quantitatively evaluating the performance of computational cognitive models against data from human behavioral experiments provides a promising avenue for building plan recognition systems.
The human brain is the most powerful plan-recognition system we know.
The base system combines a geometric place recognition capability utilizing odometry and roadmaps with a visual place recognition system.
Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
The Home Office is planning to buy a £12.8m facial recognition system to help determine an applicant's entitlement and eligibility for a British passport.
And by this summer, the FBI plans to have a fully operational face recognition system that will eventually contain upwards of 52 million records.
The TraumAID system tries to address this by providing tools for reasoning, planning, plan recognition and text generation which essentially coordinate and integrate multiple recommendations from multiple protocols.
There is a significant focus on natural language dialogue where consultation systems can become active, incorporating user modfelling, natural language generation and plan recognition, modelling metaphors, and users' mistaken beliefs.
The recognition system has been based on and inspired by an established health promotion planning model and an outline for evaluation of complex interventions [ 3– 6].
For instance, diagnosis, natural language interpretation and plan recognition.
We present the PHATT algorithm for plan recognition.
