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首页> 外文期刊>Cognitive Systems Research >Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs
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Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs

机译：通过嵌套条件进行推理：使用概率程序对心智模型进行建模

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摘要

A wide range of human reasoning patterns can be explained as conditioning in probabilistic models; however, conditioning has traditionally been viewed as an operation applied to such models, not represented in such models. We describe how probabilistic programs can explicitly represent conditioning as part of a model. This enables us to describe reasoning about others' reasoning using nested conditioning. Much of human reasoning is about the beliefs, desires, and intentions of other people; we use probabilistic programs to formalize these inferences in a way that captures the flexibility and inherent uncertainty of reasoning about other agents. We express examples from game theory, artificial intelligence, and linguistics as recursive probabilistic programs and illustrate how this representation language makes it easy to explore new directions in each of these fields. We discuss the algorithmic challenges posed by these kinds of models and describe how dynamic programming techniques can help address these challenges.
机译：各种各样的人类推理模式可以解释为概率模型中的条件。然而，传统上将调节视为应用于此类模型的操作，而不是在此类模型中表示。我们描述了概率程序如何将条件作为模型的一部分显式表示。这使我们能够使用嵌套条件来描述关于其他人的推理的推理。人类的大部分推理都与他人的信念，欲望和意图有关。我们使用概率程序将这些推论形式化，以获取关于其他主体推理的灵活性和内在不确定性。我们将博弈论，人工智能和语言学中的示例作为递归概率程序进行表示，并说明这种表示语言如何使在这些领域中的每个领域探索新方向变得容易。我们讨论了这类模型带来的算法挑战，并描述了动态编程技术如何帮助解决这些挑战。

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