Integrating Planning and Search Methods to Solve Constraint Problems

该项目加速了一项重要技术的采用：约束满足编程。许多大规模的，现实世界中的问题，如设计和配置，规划和调度，诊断和测试领域很容易理解，表示，并解决约束满足问题。尽管有很多好的通用方法，但每个新的大规模约束满足问题都面临着同样的瓶颈：稀缺的人类专家必须选择、联合收割机并改进当前可用的各种技术。该项目提高了人和机器解决新的约束满足问题的能力。该项目开发了一个系统，该系统既是研究人员的实验工具，也是真实的问题的解决方案。它学习并计划解决特定类别的约束满足问题。它结合和适应了广泛的已知约束方法，并探索新的方法，以解决困难的问题。学习支持自适应求解器，使自己适合一个问题类。规划支持更强的解决者，并体现了更高层次的洞察力的解决过程。这个项目的地址，无论是在其设计和实施，在发现和应用知识解决问题的重要问题。预期成果包括一个互动和计划数据库、一个促进新方法和计划的整合和评估的开发环境，以及一个学习新方法和制定新计划的工具。更广泛的影响包括改进调度算法，升级全球约束编程的计算基础设施，以及在一个以女性和少数民族为主的机构中培训学生。

This project speeds the uptake of an important technology: constraint satisfaction programming. Many large-scale, real-world problems in areas such as design and configuration, planning and scheduling, and diagnosis and testing are readily understood, represented, and solved as constraint satisfaction problems. Despite a wealth of good, general-purpose methods, each new, large-scale constraint satisfaction problem faces the same bottleneck: scarce human experts must select, combine, and refine the various techniques currently available. This project increases the ability of both people and machines to address new constraint satisfaction problems.The project develops a system that is both an experimental tool for researchers and a problem solver for real problems. It learns and plans to solve particular classes of constraint satisfaction problems. It combines and adapts a broad range of known constraint methods, and explores new ones, to reason about solving hard problems. Learning supports adaptive solvers that tailor themselves to a problem class. Planning supports stronger solvers, and embodies higher-level insights into the solution process.This project addresses, both in its design and its implementation, important questions in the discovery and application of knowledge to problem solving. Expected outcomes include a database of interactions and plans, a development environment that facilitates the integration and evaluation of new methods and plans, and a tool for learning new methods and developing new plans. Broader impacts include improved scheduling algorithms, upgrades to the computation infrastructure for constraint programming worldwide, and the training of students in a predominantly female and minority institution.