A methodology for using a default and abductive reasoning system
A methodology for using a default and abductive reasoning system
复制标题
使用默认和归纳推理系统的方法
DOI：
10.1002/int.4550050506
复制
发表时间：
1989
影响因子：
7
通讯作者：
D. Poole
中科院分区：
文献类型：
--
作者：
D. Poole
本文研究了两种涉及做出假设的不同活动：预测人们期望的真实情况和解释观察结果。在一篇配套文章中，提出了一种用于预测和解释的基于逻辑的架构，并概述了实现。在本文中，我们展示了如何使用这样的假设推理系统来解决识别、诊断和预测问题。其中的一部分是假设默认推理器必须经过“编程”才能获得正确的答案，而这不仅仅是“陈述真实情况”并希望系统能够神奇地找到正确答案的问题。在实践中发现许多区别很重要：预测某事物是否为真与解释其为何为真之间；以及传统默认值（作为通信约定的假设）、正态性默认值（为权宜之计而假设）和猜想（仅在有证据时才假设）之间的关系。介绍了这些区别对识别和预测问题的影响。给出了正在运行的系统的示例。
This article investigates two different activities that involve making assumptions: predicting what one expects to be true and explaining observations. In a companion article, a logic‐based architecture for both prediction and explanation is proposed and an implementation is outlined. In this article, we show how such a hypothetical reasoning system can be used to solve recognition, diagnostic, and prediction problems. As part of this is the assumption that the default reasoner must be “programmed” to get the right answer and it is not just a matter of “stating what is true” and hoping the system will magically find the right answer. A number of distinctions have been found in practice to be important: between predicting whether something is expected to be true versus explaining why it is true; and between conventional defaults (assumptions as a communication convention), normality defaults (assumed for expediency), and conjectures (assumed only if there is evidence). the effects of these distinctions on recognition and prediction problems are presented. Examples from a running system are given.