Accounting for temporal evolutions in highly reactive decision-making

Applications such as aircraft combat simulation require both dynamic supervision and real-time decision-making: actions and observations interact in a reactive way, and durations between two events must be accounted for. We start from a decision-tree model, which owns strong context handling capabilities, but provides only static decision-making. We improve it by matching possible temporal chronicles, accounting for dynamic evolutions, to the context. A decision in this framework is activated upon complete recognition of such a chronicle, whereas our highly reactive application domain requires anticipated decisions, comparing possible evolutions to take the best decision in real-time. We choose to dynamically compute a timed game automaton synthesizing the predicted possible next steps, and propose an algorithm that computes the best decision from this simple controller model.