RIA: Optimal Resource-Bounded Reasoning Using Compilation and Monitoring of Anytime Algorithms

该项目旨在开发一种决策理论，自适应的方法来构建能够稳健地执行各种实时任务的系统。这些任务包括医疗诊断、作业调度和机器人导航。在几乎所有情况下，选择最优行为所需的深思熟虑都会降低系统的整体效用。现在大家都很清楚，一个成功的系统必须权衡决策质量和审议成本。在过去的几年里，Dean、Horvitz、Russell、Zilberstein等人的工作表明，随时算法是实时系统设计的一个有用工具，因为它们允许从较小的、可重用的随时模块中计算更大的系统。本文提出的解决方案基于一种新颖的离线编译过程和运行时监控，可以最大限度地提高系统的整体效用。这项研究将产生一种新的资源有限推理体系结构。本研究将涵盖建构任意时间演算法的问题、条件效能档案的表示与操作，以及为由任意时间演算法组成的系统开发有效的编译与监控程序。

9409827 Zilberstein This project is aimed at developing a decision-theoretic, adaptive approach to building systems that can perform robustly a variety of real-time tasks. Such tasks include medical diagnosis job scheduling, and robot navigation. In almost all cases, the deliberation required to select optimal actions degrades the system's overall utility. It is by now well-understood that a successful system must trade off decision quality for deliberation cost. Over the past several years, work by Dean, Horvitz, Russell, Zilberstein, and others has shown that anytime algorithms are a useful tool for real-time system design, since they allow computation time to be larger systems from smaller, reusable anytime modules. The proposed solution to this problem is based on novel off-line compilation process and run-time monitoring that can maximize the overall utility of the system. This research will produce a new architecture for resource-bounded reasoning. The study will cover the problem of constructing anytime algorithms, the representation and manipulation of conditional performance profiles, and the development of efficient compilation and monitoring procedures for systems composed of anytime algorithms.