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。提出的研究旨在推进ppl的理论和实践，推动ppl成为基于模型的人工智能的常规和易于理解的技术，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落在ppl的舒适区之外。一个经典的例子是给一组机器人编程，让它们对环境和彼此进行不信任的推理，以便在安全关键的环境中协商行动路线。解决这些具有挑战性的问题需要现有ppp词汇表中根本没有的语言特性，并且需要实现编译时效率、推理时效率和安全性的语言实现。提出的研究将确定应对挑战所需的关键语言改进，提出新的ppp设计，并研究其语义和实现。这项研究将影响程序员使用ppp和工程人工智能的方式。自出现以来，ppl的核心吸引力一直是表达性建模语言和推理自动化。通过以新的方式解决这些诉求，这些新方式受到实际问题的驱动，并得到理论严谨性的支持，拟议的研究将使ppp在建模和解决与现实世界相关的人工智能问题所需的表现力、保证性和效率方面具有竞争优势。这项研究将产生具有开源实现和机械化元理论的新编程语言，对编程语言和人工智能社区有很大的价值。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，能够领导以编程语言为关键成分的未来技术创新。

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.