课题基金 / 基金详情

Representationally Complete Analogical - Reasoning Systems and Steps Toward Their Application in Political Science

Representationally Complete Analogical - Reasoning Systems and Steps Toward Their Application in Political Science
代表性完整的类比推理系统及其在政治学中的应用步骤
批准号:
0413206
负责人:
Patrick Winston
金额:
$44.64万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-06-01 至 2008-05-31

项目摘要

项目成果

Patrick Winston的其他基金

相似基金

相关文献

中文摘要
翻译
这个项目解决了解决问题的一个方面,这对机器和人类都是关键:通过类比和相关先例进行推理。拟议研究的目标是一种基于先例的类比推理的计算模型,使用一套简单但具有表现力和认知似是而非的表征,捕捉关于实体、类别、原因、价值和沿着真实和抽象轨迹的运动的知识。该项目的目标领域是政治学,在情报分析、国际关系和政府政策等领域,专家们使用这种基于案例的推理来分析、评估新情况,并根据相关过去的经验提出建议。该项目建立在NSF赞助的Bridge系统中开始的类比研究基础上,将利用专家分析过的大量有文件记录的政治情景和类比。虽然这个项目主要侧重于开发关于根据经验进行类比推理的计算理论,但将建立一个原型工具,旨在通过处理分析专长的一个重要方面--利用先例推理发现意外后果的能力--来帮助负责评估行动方案的分析师。原型的这个“错误拦截器”组件将监视情况和建议的行动,将这些与先例数据库进行匹配,并在适当的情况下建议分析员检查相关的先例,以寻找可能被忽略的先例所建议的可能性。为了解决计算机科学和社会科学都感兴趣的问题,这个项目将有助于一般的基于先例的推理理论,特别是政治学中的行动过程分析。
英文摘要
This project addresses an aspect of problem solving that is key for both machines and humans: reasoning by analogy with relevant precedents. The goal of the proposed research is a computational model of precedent-based analogical reasoning using a simple but expressive and cognitively plausible suite of representations that capture knowledge about entities, classes, causes, values, and motion along real and abstract trajectories. The project's target domain is political science, where in areas such as intelligence analysis, international relations, and government policy, experts use this type of case-based reasoning to analyze, assess, and make recommendations about new situations based on relevant past experience. The project, which builds on analogy research begun in the NSF-sponsored Bridge System, will make use of collections of well-documented political scenarios and analogies that have been analyzed by experts. While this project focuses primarily on development of a computational theory about reasoning by analogy from experience, a prototype tool will be built that is intended to assist analysts charged with evaluating courses of action by addressing an important aspect of analytical expertise: the ability to detect unintended consequences with precedential reasoning. This "blunder stopper" component of the prototype will monitor situations and proposed actions, match these against a data base of precedents, and, if appropriate, advise the analyst to examine a relevant precedent for possibilities suggested by the precedent that might otherwise be overlooked. In addressing a problem of interest to both computer science and social science, this project will contribute to theories of precedent-based reasoning in general, and course of action analysis in political science in particular.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
RI: Small: Enabling robust visual intelligence using propagators to model human competence
Workshop on Multi-spectrum Metrics for Cyber Defense
Workshop on International Strategy and Policy for Cyber Security
SGER: Explorations in Fine-Grained Security for Host and Network Applications
海外基金