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The authors present an embedded DSL to support Adaptation-Based Programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. They show how their design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. They also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs.
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