智能选择和选择单子

M. Abadi, G. Plotkin
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引用次数: 3

摘要

根据选择及其产生的成本和回报来描述系统,可以使算法设计者和程序员免于指定如何做出这些选择。在实现中,选择可以通过优化或机器学习方法来实现。我们从编程语言的角度来研究这种方法。我们定义了一种支持决策抽象、奖励和概率的小语言。我们给出了一个全局优化的操作语义,并使用选择单子进行决策,三个指称语义与辅助单子奖励和概率;这三个模型在回报值和期望回报之间建立了不同的相关性。我们通过证明充分性定理来证明这两种语义重合;通过证明完全抽象定理,我们证明了观测等价的特征是语义等价(在基本类型上);我们讨论程序方程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Choices and the Selection Monad
Describing systems in terms of choices and their resulting costs and rewards promises to free algorithm designers and programmers from specifying how to make those choices. In implementations, the choices can be realized by optimization or machine-learning methods.We study this approach from a programming-language perspective. We define a small language that supports decision-making abstraction, rewards, and probabilities. We give a globally optimizing operational semantics, and, using the selection monad for decision-making, three denotational semantics with auxiliary monads for reward and probability; the three model various correlations between returned values and expected rewards. We show the two kinds of semantics coincide by proving adequacy theorems; we show that observational equivalence is characterized by semantic equality (at basic types) by proving full abstraction theorems; and we discuss program equations.
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