Control Issues in Asynchronous Parallel Knowledge-Based AI Programs

小行星9321324 控制，智能选择要执行的动作，是基于知识的系统，如计划器，车间调度器和语音理解系统的性能至关重要。 这对于控制代理具有最大化并行资源利用率的附加目标的并行系统尤其如此。 拟议的研究研究并行知识为基础的系统中的控制问题，通过控制理论的发展基于任务的相互作用和环境的影响的分析。 控制问题的理论方法是基于这样一种观察，即控制是一种基于对问题解决过程、问题域和问题解决事件发生的特定环境之间相互作用的理解的知识。 这一努力将是以前的努力，开发一个正式的模型，一个域的结构如何影响广告约束的选择，用于开发解决方案，在该域和开发随机环境模型的方法的延伸。 这些努力将扩展到建模效果，特别是在并行计算中显着。 具体而言，任务的相互作用的性质将进行探讨。

9321324 Lesser Control, the intelligent selection of actions to execute, is critical to the performance of knowledge-based systems such a planners job shop schedulers, and speech understanding systems. This is particularly true for parallel systems in which the controlling agent has the additional goals of maximizing the utilization of parallel resources. The proposed research studies the issues of control in parallel knowledge-based systems through the development of a theory of control based o an analysis of task interactions and the influence of the environment. The theoretical approach to control issues is based on the observation that control is a knowledge-based on an understanding of the interactions between the problem-solving process, the problem domain, and the specific environment in which a problem-solving episode takes place. This effort will be an extension of previous efforts to develop a formal model of how the structure of a domain influences ad constrains the choice of methods used to develop solutions within that domain and in developing stochastic models of environments. These efforts will be extended towards modeling effects that are particularly significant in parallel computations. Specifically, the nature of task interactions will be explored.