We propose the LEMAIO multi-layer framework, which makes use of hierarchical
abstraction to learn models for activities involving multiple interacting objects
from time sequences of data concerning the individual objects.
Experiments in the sea navigation domain yielded learned models that were then successfully applied to activity recognition, activity simulation and multi-target tracking. Our method compares favourably with respect to previously reported results using Hidden Markov Models and Relational Particle Filtering.