Title
Logical Aspects of Several Bottom-Up Fittings
Abstract
This research is aimed at giving a bridge between the two research areas, Inductive Logic Programming and Computational Learning. We focus our attention on four fittings (learning methods) invented in the two areas: Saturant Generalization, V*-operation with Generalization, Bottom Generalization, and Inverse Entailment. Firstly we show that each of them can be represented as an instance of a common schema. Secondly we compare the four fittings. By modifying Jung's result, we show that all definite hypotheses derived by V*-operation with Generalization can be derived by Bottom Generalization and vice versa, but that some hypotheses cannot be derived by Saturant Generalization. We also give a hypotheses of a general clause which can be derived Bottom Generalization but not by V*-operation with Generalization. We show Inverse Entailment is more powerful than other three fittings both in definite and in general clausal logic. In our papers presented at the IJCAI'97 workshops and the 7th ILP workshop, Bottom Generalization was called "Inverse Entailment," but after the workshops we found it differs from Muggleton's original Inverse Entailment. We renamed it "Bottom Generalization" in order to reduce confusion and allow fair comparison of the fitting to others.
Year
DOI
Venue
1998
10.1007/3-540-49730-7_12
ALT
Keywords
Field
DocType
ilp workshop,general clausal logic,computational learning,saturant generalization,bottom-up fittings,general clause,research area,logical aspects,definite hypothesis,bottom generalization,inverse entailment,original inverse entailment,bottom up
Inductive logic programming,Inverse,Universal generalization,Logical consequence,Formal language,Generalization,Computer science,Top-down and bottom-up design,Philosophy of language,Artificial intelligence,Machine learning
Conference
Volume
ISSN
ISBN
1501
0302-9743
3-540-65013-X
Citations 
PageRank 
References 
2
0.43
11
Authors
1
Name
Order
Citations
PageRank
Akihiro Yamamoto19511.23