Case-Based Planning and Execution in a Time-Critical Environment

该项目将设计、实施和评估一个集成了计划、执行和实时学习的系统。本研究的四个主要目标是：(1)开发一个可用的执行监视器，实时控制计划的执行；(2)发展直接从记忆片段中进行计划的方法；(3)自动获取执行案件；(4)开发使用弱域理论的学习技术。该系统的领域是膳食计划和准备。该系统将计划和协助准备膳食，然后将这些结果纳入其案例存储器。这里的中心问题是设计一个足够有效的系统来实时运行。这将部分通过对时间关键型流程施加计算限制来实现，部分通过使用基于案例的方法来生成和修复计划，该方法使用先前执行的情景表示。由此产生的计划和修复中的细节简化了执行监视和案例获取。学习是在非关键时刻完成的，并且是在没有强大的领域理论的情况下完成的，因为它将期望失败归因于用于产生期望的假设中的失败。

This project will design, implement, and evaluate a system that integrates planning, execution, and learning in real time. The four main objectives to this research are: (1) Develop a usable execution monitor that will control plan execution in real time; (2) Develop the methods to plan directly from episodes in memory;(3) Automate the acquisition of cases from execution; and (4) Develop techniques for learning using a weak domain theory. The system's domain is meal planning and preparation. The system will plan and assist in the preparation of meals, then incorporate the results of these into its case memory. A central concern here is to design a system that is efficient enough to operate in real time. This will be attained in part by placing computational restrictions on time-critical processes, and in part by using a case-based approach to plan generation and repair that use episodic representations of previous executions. The detail in the resultant plans and repairs simplifies both execution monitoring and case acquisition. Learning is done at noncritical times, and is done without a strong domain theory by attributing expectation failures to failures in the assumptions used to generate the expectations.