Ngo L, Haddawy P
Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee 53201, USA.
Proc AMIA Annu Fall Symp. 1996:254-8.
We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.
我们提出了一个用于表示行动的概率效应和应急治疗计划的框架。我们的语言具有明确的声明性语义，并且我们已经开发了一种实现的算法（名为BNG），该算法生成贝叶斯网络（BN）以计算查询的后验概率。在本文中，我们通过自动构建相互关联的BN结构（我们称之为BN图）并在其上应用可用的传播程序来解决预测应急治疗计划的问题。为了解决最优计划生成问题，我们的方法基于这样的观察，即通常目标计划空间具有明确的结构。我们提供一种类似于带有循环和条件语句的编程语言的语言来描述计划空间。我们简要介绍了从此类指定计划空间中找到最优计划的过程。
