Design of Multiprocessing Systems for Learning Strategies

学习策略多处理系统的设计

• 批准号: 8810584
• 负责人: Benjamin Wah
• 金额: $ 30.15万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-12-01 至 1992-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8810584&HistoricalAwards=false
• 关键词: Design; Multiprocessing; Systems; Learning; Strategies

The goal of this research is to develop a multiprocessing system that automatically learns strategies for dynamic decision problems, both in hardware and software, such that the strategy learned is targeted for a multiprocessing system, and that the strategy is the best among those that can be found in the given time and resource constraints of the learning system and the knowledge provided by the users. The class of problems being explored is called dynamic decision problems. These problems may possess one or more of the following properties: the decisions necessary to solve the problem may be interrelated, the decisions are based on dynamic parameters which may be uncertain, the number of possible strategies is very large, and the effects of a decision may not be immediately available after the decision is made. A dynamic decision problem is solved by a combination of domain knowledge and meta-knowledge. Domain knowledge consists of the definition of the problem and its parameters, its representation, and possibly an algorithm (or class of algorithms) to solve it. Meta-level knowledge is knowledge about selecting alternatives to implement in the algorithm and its representation, or finding new ways of solving the given problem. Domain knowledge can be implemented in the form of a program in a computer, for instance, while meta-knowledge is provided by the designers. This research focuses on the development of methods so that some of the meta-knowledge supplied by the designers can be obtained by automatic learning methods. Initially, the focus is on dynamic decision problems for which there are some known solutions, and on learning strategies to solve these problems. The dynamic decision problems addressed include combinatorial search problems and real-time decision problems. The distinguishing features of this research are as follows: First, architectural constraints are being included so that the learning system will find the best strategy given a fixed amount of time and resources, and the computer on which the learning system is implemented. Second, the strategy learned by the learning system also includes consideration of the computer on which it will be implemented. The objectives of this research are to develop methods for automatic learning and to study multiprocessor architectures suitable for learning systems. This area of research is very important and timely. The investigator is well qualified to direct this research. Support is highly recommended.

本研究的目标是开发一个多处理系统， 自动学习动态决策问题的策略， 硬件和软件，这样学习的策略是针对一个 多处理系统，并且该策略是其中最好的 在给定的时间和资源限制下， 学习系统和用户提供的知识。 正在探索的这类问题称为动态决策 问题 这些问题可能具有以下一个或多个方面 属性：解决问题所需的决策可能是 相互关联，决策基于动态参数， 不确定，可能的策略数量非常大， 一项决定的效果可能不会立即提供后， 已经决定了。 动态决策问题是通过域的组合来解决的 知识和元知识。 领域知识包括 问题的定义及其参数，其表示，以及 可能是一个算法（或一类算法）来解决它。 知识是关于选择替代方案的知识， 算法及其表示，或寻找新的方法来解决 给定的问题。 领域知识可以以 例如，当提供元知识时， 由设计师设计。 这项研究的重点是发展的方法，使一些 由设计者提供的元知识可以通过自动 学习方法 最初，重点是动态决策问题 有一些已知的解决方案，以及学习策略， 解决这些问题。 所解决的动态决策问题包括 组合搜索问题和实时决策问题。 本研究的特点如下：第一， 体系结构的限制被包括在内， 系统将在给定的时间内找到最佳策略， 资源，以及学习系统所在的计算机 切实贯彻 第二，学习系统学习到的策略也 包括考虑将在其上实现的计算机。 本研究的目的是开发自动化的方法， 学习和研究多处理器体系结构， 学习系统。 这个研究领域非常重要和及时。 研究者完全有资格指导本研究。 支持是 强烈推荐