On learning spatio-temporal relational structures in two different domains

Abstract

In this paper we consider the types of representations and learning procedures required to construct rules which can adequately describe relational information as it occurs in spatio-temporal sequences. A comparison of interpreting on-line hand drawings is made to the automatic generation of flight manoeuvre description based on a relational learning system we have developed, the Consolidated Learning Algorithm based on Relational Evidence Theory (CLARET). The package adapts relational learning techniques to utilise the constraints present in time series data. Our approach involves supporting queries, automatic descriptions and/or predictions from spatio-temporal action sequences.