Research on Real-Time Decision Making: The RALPH Project

实时决策研究：RALPH 项目

• 批准号: 8903146
• 负责人: Stuart Russell
• 金额: $ 30.2万
• 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: 1989
• 资助国家: 美国
• 起止时间: 1989-08-01 至 1992-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8903146&HistoricalAwards=false
• 关键词: Research; Real; Time; Decision; Making

The objective of the proposed research is to understand the effect of limited computational resources on the design of optimal decision making sytems, so as to be able to build agents that are robust in the face of complexity, and capable of acting in real time. The theoretical phase involves the completion of a normative theory for reasoning about computations. The approach is to treat computations as actions, with outcomes of varying probabilities and utilities using decision theory to choose between them. The theory will be incorporated in a multilevel decision theoretic architecture, which extends the idea of explicit metalevel reasoning to cover recursively all aspects of the decision making process. The approach has been applied successfully to game playing systems, and will be modified to apply to single agent problems such as robot motion planning. However, metalevel reasoning to often expensive, and the cost can only be amortized over future problem solving if a compilation mechanism is available. The investigator will therefore study methods for compilation of reasoning processes occuring anywhere in the hierarchy. He will extend explanation based learning techniques to cover the case of choosing best actions, where probabilistic time accuracy tradeoffs play a significant role.

这项研究的目的是了解有限的计算资源对最优决策系统设计的影响，以便能够建立面对复杂情况下的健壮性和能够实时行动的代理。理论阶段包括完成关于计算推理的规范理论。方法是将计算视为行动，具有不同概率和效用的结果，并使用决策理论在它们之间进行选择。该理论将被纳入一个多层次决策理论体系中，该体系将显式元水平推理的思想扩展到递归地覆盖决策过程的所有方面。该方法已成功地应用于游戏系统中，并将被改进应用于机器人运动规划等单智能体问题。然而，元层次的推理往往代价高昂，而且只有在有编译机制的情况下，成本才能在未来的问题解决中摊销。因此，调查者将研究在层次结构中的任何地方发生的推理过程的汇编方法。他将扩展基于解释的学习技术，以涵盖选择最佳动作的情况，其中概率时间精度权衡发挥着重要作用。