Missing Interplanetary Data Estimation for Space Missions via Symbolic Rule Induction

The necessity to limit budget, size, weight and power consumption of the instruments placed on board space mission satellites results in several drawbacks, including the exclusion of dedicated instrumentation for the monitoring of the spacecraft environment. Understanding the environmental conditions of space missions is essential to correctly analyse their observations. Seldom the necessary interplanetary parameters, not measured in situ, can be gathered from nearby dedicated missions, however this is not always feasible. Other solutions envisage the application of machine learning models to estimate the missing parameters on the basis of those that are available on board the satellites. Despite the high performance of machine learning predictors, they come along with issues related to the model selection and training, the data pre-processing and the opaqueness of the outcomes returned to end-users. The application of tools developed in the explainable artificial intelligence (XAI) field can be considered to encode through symbolic knowledge the functional relationship between parameters observed in situ and correlated parameters for which measurements are lacking but useful. In this context, XAI methods in general, and symbolic knowledge extraction in particular, constitute a promising alternative to traditional machine learning models, enabling users to avoid the model selection and training phases and to obtain completely interpretable results. This presentation provides an overview on the application of symbolic knowledge-extraction techniques to perform rule induction from available in-situ data, aimed at carrying out a human-interpretable estimation and forecasting of missing platform parameters. Potentialities, drawbacks and challenges of this approach are discussed to highlight the direction from current results to future applications.