Research on Real-Time Decision Making: The Ralph Project

实时决策研究：拉尔夫项目

• 批准号: 9211512
• 负责人: Stuart Russell
• 金额: $ 34.68万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-01-15 至 1996-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9211512&HistoricalAwards=false
• 关键词: Research; Real; Time; Decision; Making

9211512 Russell This is a REU supplemental award to IRI-9211512. The objective of the RALPH (Rational Agents with Limited Performance Hardware) project is to understand the effect of limited computational resources on the design of optimal decision-making systems. Work to date has specified a normative theory of metareasoning to control computation, and has applied it to several combinatorial problem classes; in all cases significant performance improvements over standard algorithms have been record. The methodology for applying the theory has been developed in detail, and has been used by other researchers to solve complex problems. The PI will continue to work on more general decision-theoretic metalevel architectures; probabilistic planning and scheduling systems; and composition of real-time systems, for which preliminary results have been obtained. A software environment will be developed that allows complex real-time agents to be constructed automatically from elementary soft-real-time components. A final element of the proposal is the construction of provably optimal bounded agents. Working initially in a class of simple rile-based machines, it will be shown that a learning algorithm can converge quickly to an approximately optimal rule set. These results will be extended to find the scope and limits of adaptive methods for achieving bounded rationality, thereby contributing to the foundation for a science of artificial intelligence.

小行星9211512 这是REU对IRI-9211512的补充裁决。 RALPH（Rational Agents with Limited Performance Hardware）项目的目标是了解有限的计算资源对最优决策系统设计的影响。 迄今为止的工作已经指定了一个规范的元推理理论来控制计算，并已将其应用到几个组合问题类，在所有情况下，显着的性能改进标准算法已被记录。 应用该理论的方法已经详细开发，并已被其他研究人员用来解决复杂的问题。 PI将继续致力于更一般的决策理论元级架构;概率规划和调度系统;和实时系统的组成，已经取得了初步成果。 将开发一个软件环境，允许从基本的软实时组件自动构建复杂的实时代理。 该建议的最后一个要素是可证明的最佳有界代理的建设。 工作最初在一类简单的基于规则的机器，它将表明，学习算法可以快速收敛到一个近似最优的规则集。 这些结果将被扩展到寻找实现有限理性的自适应方法的范围和限制，从而为人工智能科学的基础做出贡献。