The text discusses the concept of Cooperative Inverse-Reinforcement Learning (CIRL), a framework designed to align machine actions with human preferences through a game-theoretic approach involving partial information. Using a hypothetical example of agents named Harriet and Robby, it illustrates how uncertainty about preferences encourages cooperation and teaching, and further applies this framework to solve the "off-switch problem" by incentivizing robots to allow themselves to be deactivated.
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