Probabilistic Tabled Logic Programming

表格解析使用memoization来解决Prolog风格解析的主要缺点，即弱终止，重复的子计算和弱否定语义。 一些需要定点计算的复杂问题，包括一些模型检测和程序分析问题，被转换为逻辑程序上的查询评估，并使用表解析法有效地解决，本项目旨在将联合收割机演绎，特别是表解析法与概率推理相结合，以促进具有概率行为的系统的声明式建模和推理。 该项目包括：（a）概率表解析语义的基础研究，（B）在一个健壮的原型中实现研究结果，以及（c）开发结合联合收割机逻辑和概率推理的应用程序，如有限状态和下推模型的概率模型检查和概率程序分析。 该项目解决了几个问题的语义水平，包括连续和离散的随机变量的混合物的程序，程序编码动态模型在无界但离散的时间，可扩展的概率推理技术（如采样）与演绎的整合治疗。 在实现层面，该项目开发了轻量级方法，用于将概率推理和参数学习纳入称为概率表逻辑编程（PTLP）的声明式编程框架。 这个框架提供了一个坚实的语义和计算基础相结合的逻辑和概率推理。 紧密集成计算逻辑、统计和约束处理的计算基础设施将对系统开发和验证、规划、物流以及优化和控制等领域产生直接影响，并在科学和工程中具有广泛的应用。

Tabled resolution uses memoization to address the major shortcomings of Prolog-style resolution, namely, weak termination, repeated subcomputations, and weak semantics for negation. Several complex problems requiring fixed-point computation, including several model checking and program analysis problems, have been cast as query evaluation over logic programs and solved efficiently using tabled resolution.This project aims to combine deduction, especially tabled resolution, with probabilistic inference in order to facilitate declarative modeling and reasoning of systems with probabilistic behavior. The project includes (a) fundamental research on the semantics of probabilistic tabled resolution, (b) implementation of the research results in a robust prototype, and (c) development of applications that combine logical and probabilistic reasoniong such as probabilistic model checking of finite-state and pushdown models and probabilistic program analysis. The project addresses several problems at the level of semantics, including the treatment of programs with a mixture of continuous and discrete random variables, programs that encode dynamic models over unbounded but discrete time, and the integration of scalable probabilistic inference techniques (e.g. sampling) with deduction. At the implementation level, the project develops light-weight methods for incorporating probabilistic inference and parameter learning into a declarative programming framework called Probabilistic Tabled Logic Programming (PTLP). This framework provides a firm semantic and computational basis for combining logical and probabilistic reasoning. A computing infrastructure that tightly integrates computational logic, statistics, and constraint processing will have an immediate impact on the areas of system development and verification, planning, logistics, and optimization and control, with broad application in science and engineering.