Integrating Problem-driven and Class-based Learning for Constraint Satisfaction

整合问题驱动和基于课堂的学习以实现约束满足

• 批准号: 0811437
• 负责人: Susan Epstein
• 金额: $ 43.33万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811437&HistoricalAwards=false
• 关键词: Integrating; Problem; driven; Class; based

Many large-scale, real-world problems are readily understood, represented, and solved as constraint satisfaction problems. Organizations throughout the world exploit this approach to solve difficult problems in design and configuration, planning and scheduling, and diagnosis and testing. Nonetheless, each new, large-scale problem faces the same bottleneck: scarce human experts must select, combine, and refine the various techniques currently available for constraint satisfaction and optimization. This cognitively-oriented project increases the ability of both people and machines to address challenging new constraint satisfaction problems.The resultant autonomous, robust system reasons from past experience, but with the ability to recognize and respond intelligently to novelty. The new approach integrates a variety of techniques to capture crucial subproblems, the most informative and conflict-ridden parts of a problem. Because crucial sub-problems often recur with only small variations, knowledge about how to solve them may be re-used. Moreover, when a problem is unsolvable, the system identifies crucial subproblems for human analysis and reformulation ? a first step toward collaborative problem solving.This project speeds the uptake of an important technology. It generates knowledge about crucial subproblems, search, representation, and learning for constraint solving, and thereby makes constraint-programming expertise more readily available. This project analyzes the efficacy of its approach on a variety of constraint problems, particularly real-world problems.

许多大规模的现实世界的问题很容易理解，表示和解决的约束满足问题。世界各地的组织利用这种方法来解决设计和配置、规划和调度以及诊断和测试中的难题。尽管如此，每一个新的大规模问题都面临着同样的瓶颈：稀缺的人类专家必须选择、联合收割机并改进目前可用于约束满足和优化的各种技术。这个认知导向的项目提高了人和机器解决具有挑战性的新约束满足问题的能力。由此产生的自主，强大的系统从过去的经验中推理，但具有识别和智能响应新奇事物的能力。新的方法集成了各种技术，以捕捉关键的子问题，最翔实和冲突的一个问题的部分。因为关键的子问题经常重复出现，只有很小的变化，关于如何解决它们的知识可以重复使用。此外，当一个问题是无法解决的，该系统确定人类分析和重新制定的关键子问题？迈向合作解决问题的第一步。2这个项目加速了一项重要技术的吸收。它产生的知识的关键子问题，搜索，表示和学习约束求解，从而使约束编程的专业知识更容易获得。该项目分析了其方法在各种约束问题上的有效性，特别是现实世界的问题。