Multi-frame modal symbolic learning

Symbolic learning is the sub-field of machine learning that deals with symbolic algorithms and models, which have been known for decades and successfully applied to a variety of contexts. The main limitation of symbolic models is the fact that they are essentially based on classical propositional logic, which implies that data with an implicit dimensional component, such as temporal (e.g., time series) or spatial data (e.g., images), cannot be properly dealt with within the standard symbolic framework. Recently, modal symbolic learning models have been proposed as a natural extension of classical ones to naturally deal with dimensional data, and successfully applied to temporal and spatial data. In this paper, we discuss the possibility of further extending such learning models to deal with multi-frame dimensional data, to be able to natively learn from instances represented by more than one dimensional description.