Design of Multiprocessing Systems for Learning Strategies

本研究的目标是开发一个多处理系统，该系统可以在硬件和软件中自动学习动态决策问题的策略，从而使所学习的策略针对多处理系统。在给定的学习系统的时间和资源约束以及用户提供的知识条件下，该策略是所有策略中最好的。这类问题被称为动态决策问题。这些问题可能具有以下一个或多个性质：解决问题所需的决策可能是相互关联的，决策基于动态参数，这些参数可能是不确定的，可能的策略数量非常大，决策做出后可能无法立即获得决策的效果。将领域知识和元知识结合起来解决动态决策问题。领域知识包括问题的定义及其参数、表示形式，以及可能的解决问题的算法（或算法类）。元级知识是关于在算法及其表示中选择实现方案的知识，或者找到解决给定问题的新方法的知识。例如，领域知识可以在计算机中以程序的形式实现，而元知识则由设计者提供。本研究的重点是开发方法，使设计人员提供的部分元知识可以通过自动学习方法获得。最初，重点是有一些已知解决方案的动态决策问题，以及解决这些问题的学习策略。动态决策问题包括组合搜索问题和实时决策问题。本研究的显著特征如下：首先，包含了架构约束，以便在给定固定的时间和资源以及实现学习系统的计算机的情况下，学习系统将找到最佳策略。其次，学习系统学习到的策略还包括将在其上实施的计算机的考虑。本研究的目标是开发自动学习的方法，并研究适合学习系统的多处理器架构。这一领域的研究是非常重要和及时的。这位研究者完全有资格指导这项研究。强烈建议提供支持。

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.