multi-armed bandit

From one-armed bandit, by analogy with a gambler at a row of slot machines who has to decide how best to play them.

multi-armed bandit (plural multi-armed bandits)

1. (probability theory, machine learning) An algorithm that allocates a fixed limited set of resources between competing alternative choices so as to maximize the expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or allocations are made.