仮説レベルを用いた物概念知識の獲得・修正手法
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概要
- 論文の詳細を見る
If a computer is to approach the same capability of understanding that humans possess, it will need many types of knowledge. One such type of knowledge pertains to the concepts of objects. There are many ways to define these concepts. One way is to define them using sets of attributes. The knowledge concerning objects and their attributes is called object-attribute knowledge. When a task domain is not restricted, describing by hand the attributes for all objects that should be described is too much work. This paper presents a method for acquiring and modifying object-attribute knowledge based on several examples indicating whether or not an object has an attribute. In this method, an existing thesaurus showing a classification hierarchy of objects is used to control object-attribute knowledge inheritance. This thesaurus does not need to be built specifically for object-attribute knowledge inheritance and is handled as a set of hypotheses which can be modified to yield an accurate inheritance. When an example is given, thesaurus hypotheses and new hypotheses concerning object-attribute knowledge are modified to make both sets of hypotheses consistent with all examples. This modification is done using three levels of hypotheses which are assigned automatically. If a contradiction occurs with a given example, the hypotheses of a lower level are modified to resolve the contradiction. If this fails to resolve the contradiction, upper-level hypotheses are modified. Whether or not an object has an attribute is inferred by using the set of hypotheses and inference rules even if no example indicating that the object has the attribute has been given. Thus, it is not necessary to describe the attributes of all objects by hand. This method is applied to acquire and modify the knowledge pertaining to the measure-attribute of objects. Measure-attribute is an attribute concerning measurable objects properties. Two different thesauruses are used for knowledge inheritance. Whichever of them is used, this method properly infers the measure-attribute of many objects from a few examples.
- 社団法人人工知能学会の論文
- 1997-05-01
著者
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飯田 敏幸
NTT(株)コミュニケーション科学研究所
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飯田 敏幸
NTTコミュニケーション科学研究所
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牧野 俊朗
Ntt(株)コミュニケーション科学基礎研究所
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牧野 俊朗
NTTコミュニケーション科学研究所
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