From Streams of Observations to Knowledge-Level Productive Predictions

Abstract

The benefit to be gained by Ambient Assisted Living (AAL) systems depends heavily on the successful recognition of human intentions. Important indicators for specific intentions are behavior and situational context. Once a sequence of actions can be associated with a specific intention, assistance may be provided by anticipating the next individual step and supporting the human in its execution. The authors present a combination of Sequence Abstraction Networks (SAN) and IGOR to guarantee early and impartial predictions with a powerful detection for symbolic regularities. They first generate a hierarchy of abstract action sequences, where individual contexts represent subgoals or minor intentions. Afterwards, they enrich this hierarchy by recursive induction. An example scenario is presented where a table needs to be set for several guests. It turns out that correct predictions can be made while still executing the observed sequence for the first time. Support can therefore be completely individual to the person being assisted but nonetheless be very dynamic and quick in anticipating the next steps.