Details

Autor(en) / Beteiligte

• SAWARAGI, T
• IWAI, S
• KATAI, O

Titel

Controlling the process of learning from an example through adaptive generalization of episodic memory

Ist Teil von

• Fuzzy sets and systems, 1991-01, Vol.39 (2), p.133-162

Ort / Verlag

Amsterdam: Elsevier

Erscheinungsjahr

1991

Quelle

Elsevier Journal Backfiles on ScienceDirect (DFG Nationallizenzen)

Beschreibungen/Notizen

• Automizing acquisition of expert knowledge is one of the most important problems to be solved for knowledge-based systems such as expert systems or decision support systems. Machine learning is a subfield of artificial intelligence concerning the theory on how to learn concepts from examples. In this field, two types of learning methods have been proposed so far: explanation-based learning (EBL) as a theory of deductive learning and similarity-based learning (SBL) as a theory of inductive learning. This paper presents a methodology to acquire general knowledge from a single example (episode) by the use of a repertoire of prior cases based on a hybrid architecture of EBL and SBL. This procedure for typicality evaluation of an episode is formulated as a fuzzy reasoning process. The system outputs a general knowledge so that it can be applied to situations other than the input case.