Thinking Backward for Knowledge Acquisition
Thinking Backward for Knowledge Acquisition
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知识获取的逆向思维
DOI：
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发表时间：
1987
期刊：
影响因子：
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通讯作者：
D. Heckerman
中科院分区：
文献类型：
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作者：
Ross D. Shachter;D. Heckerman
本文探讨了在建立专家系统时为诊断和决策而构建知识库的方向。传统上，从专家获取知识的方向是从可观察的证据到不可观察的假设。然而，专家通常发现从相反的方向推理更简单--从假设到不可观察的证据--因为这个方向反映了因果关系。因此，我们主张按照专家的自然推理方向来构建知识库，然后颠倒方向，使用这种表示方向的选择有助于确定领域中的知识获取，并且在涉及不确定性的问题时是必不可少的。一种用于以图形表示联合概率分布影响图的方法提供了一种实用的方法，通过该方法，专家可以在适当的方向上表征证据和假设之间的定性和定量关系。一旦构建，这些关系可以容易地反转到较不直观的方向，以便执行推理和诊断，这样，知识获取在认知上变得简单；这台机器承担着翻译表示法的重担几年前，我们与一位从事计算机视觉工作的同事讨论概率推理时，他想计算给定数字化图像时老虎出现在视野中的可能性。“好的，”我们回答道，“如果老虎在场，你看到这张照片的可能性有多大？”另一方面，如果老虎不在现场，你看到它的可能性有多大？在我们可以说“首先有老虎的可能性有多大？”我们的同事绝望地张开双臂：“为什么你们这些概率论者一定要把一切都往后想？”从那时起，我们一直在思考这个问题。为什么我们要向后看证据推理的问题？毕竟，我们的任务是
This article examines the direction in which knowledge bases are constructed for diagnosis and decision making When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses However, experts usually find it simpler to reason in the opposite direction-from hypotheses to unobservable evidence-because this direction reflects causal relationships Therefore, we argue that a knowledge base be constructed following the expert’s natural reasoning direction, and then reverse the direction for use This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty We illustrate this concept with influence diagrams, a methodology for graphically representing a joint probability distribution Influence diagrams provide a practical means by which an expert can characterize the qualitative and quantitative relationships among evidence and hypotheses in the appropriate direction Once constructed, the relationships can easily be reversed into the less intuitive direction in order to perform inference and diagnosis, In this way, knowledge acquisition is made cognitively simple; the machine carries the burden of translating the representation A few years ago, we were discussing probabilistic reasoning with a colleague who works in computer vision He wanted to calculate the likelihood of a tiger being present in a field of view given the digitized image. “OK,” we replied, “If the tiger were present, what is the probability that you would see that image? On the other hand, if the tiger were not present, what is the probability you would see it?” Before we could say “what is the probability there is a tiger in the first place?” our colleague threw up his arms in despair “Why must you probabilists insist on thinking about everything backwards?” Since then, we have pondered this question. Why is it that we want to look at problems of evidential reasoning backward? After all, the task of