Semantics and Implementation of Probabilistic Programming Languages

概率编程语言（PPL）越来越多地用于建模和解决日益复杂的现实世界人工智能（AI）问题。拟议的研究旨在推进PPL的理论和实践，推动PPL成为基于模型的AI技术的未来，就像传统编程语言用于传统软件开发一样。尽管最近在概率编程方面取得了进展，但许多潜在的有影响力的应用程序仍然超出了PPL的舒适区。一个经典的例子是，对一组机器人进行编程，这些机器人对环境和彼此不信任地进行推理，以便在安全关键的环境中协商一系列行动。解决这些具有挑战性的问题将需要现有PPL词汇表中根本没有的语言特征，并需要实现编译时效率，推理时效率和安全性的语言实现。拟议的研究将确定应对挑战所需的关键语言改进，提出PPL的新设计，并研究其语义和实现。这项研究将影响程序员使用PPL和工程师人工智能的方式。PPL的核心诉求，自其出现以来，一直是表达性建模语言和推理的自动化。通过以新的方式解决这些诉求，这些方式受到实际问题的激励并得到理论严谨性的支持，拟议的研究将使PPL在建模和解决现实世界相关AI问题所需的表达力，保证和效率方面具有竞争优势。这项研究将产生具有开源实现的新编程语言以及机械化的元理论，对编程语言和人工智能社区具有重要价值。参与这项研究的学员将成长为编程语言研究和新兴的概率编程领域的专家，能够引领未来的技术创新，将编程语言作为关键因素。

Probabilistic programming languages (PPLs) are increasingly used to model and solve real-world artificial intelligence (AI) problems of growing complexity. The proposed research aims to advance both the theory and practice of PPLs, pushing towards a future where PPLs become as regular and well-understood a technology for model-based AI as conventional programming languages are for traditional software development. Despite recent advances in probabilistic programming, many potentially impactful applications still fall outside the comfort zone of PPLs. A classic example is programming a group of robots that reason distrustfully about the environment and about each other to negotiate a course of actions in a safety-critical setting. Solving such challenging problems would require linguistic features that are simply not in the vocabulary of existing PPLs and demand language implementations that achieve compile-time efficiency, inference-time efficiency, and security. The proposed research will identify the key language improvements needed to address the challenges, propose new designs of PPLs, and study their semantics and implementations. This research will impact the way programmers use PPLs and engineer artificial intelligence. The core appeals of PPLs, since its advent, have been expressive modeling languages and the automation of inference. By addressing these appeals in new ways that are motivated by practical concerns and backed by theoretical rigour, the proposed research will give PPLs a competitive edge in terms of expressiveness, assurance, and efficiency needed in modeling and solving AI problems of real-world relevance. This research will produce new programming languages with open-sourced implementations as well as mechanized metatheories, of value to programming languages and AI communities in the large. Trainees involved in this research will grow to become experts in programming languages research and in the burgeoning field of probabilistic programming, capable of leading future technology innovations that involve programming languages as a key ingredient.