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.
