RUI: Strategies for Fast Execution of Constraint Logic Programs Over a Real-Number Domain

RUI：在实数域上快速执行约束逻辑程序的策略

• 批准号: 9408298
• 负责人: Jennifer Burg
• 金额: --
• 依托单位: Wake Forest University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-04-01 至 1997-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9408298&HistoricalAwards=false
• 关键词: RUI; Strategies; Fast; Execution; Constraint

Constraint logic programming is a declarative programming paradigm which has proven to be expressive for computationally difficult problems such as scheduling, air traffic flow management, simulation of dynamic physical systems, solution of differential equations, and options trading analysis. However, while CLP languages are expressive, their execution is not always efficient enough for the problems to which they apply. The goal of this research is to develop strategies for fast execution of constraint logic programs, particularly in the domain of real or rational arithmetic constraints. To this end, incremental Gaussian elimination-based and simplex-based constraint satisfaction algorithms are compared for their efficiency in the context of CLP; the feasibility of intelligent backtracking and independent and-parallelism is investigated; work on asynchronous parallel execution of the inference engine and solver is continued; and parallel incremental algorithms for constraint solving are being developed. The research program has strong undergraduate involvement and research components. Students are involved in the design, implementation, and testing of algorithms, and are exposed to concepts from the theory of computation, programming language design, the linear programming problem, AI search and backtracking strategies, and parallel computation.

约束逻辑编程是一种声明式编程范式，它已被证明是表达计算困难的问题，如调度，空中交通流量管理，动态物理系统的模拟，微分方程的解，和期权交易分析。然而，虽然CLP语言是表达性的，但它们的执行对于它们所应用的问题来说并不总是足够有效。本研究的目标是开发策略，快速执行的约束逻辑程序，特别是在域的真实的或合理的算术约束。为此，增量高斯消除为基础的和单纯形为基础的约束满足算法进行了比较，他们的效率在CLP的上下文中;智能回溯和独立和并行的可行性进行了研究;异步并行执行的推理机和求解器的工作继续进行;和并行增量算法约束求解正在开发中。该研究计划有很强的本科生参与和研究组成部分。学生参与算法的设计，实现和测试，并接触到计算理论，编程语言设计，线性规划问题，AI搜索和回溯策略以及并行计算的概念。