MineApprentice

Automated planners have been adopted as key decision support tools for mission planning in a wide range of domains, such as maritime Mine CounterMeasures (MCM). However, the models that power these planners, such as models of capabilities, tactics, environment, and goals, must currently be hand-codeda tedious and slow process that makes model maintenance impractical for highly dynamic domains where models must be constantly updated. Further, the hand-coding approach does not scale to scenarios with increasing numbers of heterogeneous unmanned assets, such as unmanned underwater vehicles, where their sensing and autonomous capabilities can vary widely and change far more rapidly than larger manned systems. In these scenarios, MCM operators cannot rely on automated planners to have adequate knowledge of assets and how best to tactically employ them because the rate at which this knowledge can be hand-coded is insufficient to keep up with the tempo of change. These limitations ultimately result in suboptimal mission performance and underutilization of unmanned assets. To overcome these challenges, the SoarTech team proposes MineApprentice, a machine learning system that incrementally and continually acquires both asset performance models and tactical planning knowledge from interaction with operators as they use existing MCM planning software (e.g., MEDAL-EA/MineNet Tactical).