Learned Adaptive Network AutomatIon (LANAI)

Future warfighting efforts are expected to be larger scale, with many coordinating platforms over larger geographic areas. Further, battle tempo will significantly increase in peer and near-peer conflicts relative to the tempos historically encountered by U.S. Forces.  This reality results in three key technical challenges.  One, Network management must be autonomous: human network engineers will not be able to manage networks which can support the scale and tempo of future operations.  Artificial Intelligence (AI) and Machine Learning (ML) techniques enable this automation. Two, Network management must be mission-driven: network management/orchestration policies should align with and enable the Commander’s Intent.  And three, Network traffic must dynamically respect Commander’s priorities: at the scale and cadence of future operations, not all desired information exchange requirements (IERs) can be supported by the network. In this proposal, Scientific Systems Company Inc. (SSCI) proposes a Learned Adaptive Network AutomatIon (LANAI) study which will provide an autonomous network traffic prioritization and orchestration capability which will maximally align communications within a network to the commander's intent during contested operations. If successful, our approach will enable a commander to quickly and easily switch the focus of network communications between dynamically changing operational needs.