Combinatorial coverage framework for machine learning in multi-domain operations

Multi-domain operations (MDO) are characterized by simultaneous and sequential operations; rapid and continuous integration; and surprise. Machine learning (ML) for MDO is no different. Translated into ML, MDO requires highly assured yet rapid data and model fusion. Assurance demands robustness, reliability, and explainability, while speed demands computational efficiency and sample efficiency. Combinatorial interaction testing offers explainable and rigorous techniques to ML for fusing data and models with runtime guarantees. But such methods are underexplored in the literature. Combinatorial coverage has been applied to neuron- and layer-levels of neural networks, but only recently to ML in general. There are also ongoing debates of efficacy in the literature, but these debates are scoped to explainable deep learning. This work presents a framework for using combinatorial coverage for multi-domain operations. We discuss how coverage metrics can incorporate multi-modal meta-data and mission context into fusion processes, how coverage is oriented towards identifying gaps in and between sets of data, and how coverage can identify cases where performance is expected to be difficult. We conclude that combinatorial coverage should be considered a core capability for supporting ML in MDO.