Covering Approach to Action Rule Learning

2018 ◽  
pp. 182-193
Author(s):  
Paweł Matyszok ◽  
Marek Sikora ◽  
Łukasz Wróbel
Keyword(s):  
Rule Learning

Effects of pretraining and stimulus composition on rule learning.

1969 ◽  
Vol 80 (3, Pt.1) ◽  
pp. 450-454 ◽  
Author(s):  
Peter J. Johnson ◽  
Roger H. White
Keyword(s):  
Rule Learning

Probabilistic Rule Learning Systems

2021 ◽  
Vol 54 (4) ◽  
pp. 1-16
Author(s):  
Abdus Salam ◽  
Rolf Schwitter ◽  
Mehmet A. Orgun
Keyword(s):  
Probability Theory ◽  
Rule Learning ◽  
Background Knowledge ◽  
Learning Systems ◽  
Future Research ◽  
Research Directions ◽  
System Architectures ◽  
Probabilistic Rule ◽  
Future Research Directions ◽  
High Level

This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.

Rule learning transfer across linguistic and visual modalities in 7‐month‐old infants

Infancy ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 442-454
Author(s):  
Hermann Bulf ◽  
Ermanno Quadrelli ◽  
Shannon Brady ◽  
Bryan Nguyen ◽  
Viola Macchi Cassia ◽  
...  
Keyword(s):  
Learning Transfer ◽  
Rule Learning

scholarly journals Intra-language: the study of L2 morpheme productivity as within-item variance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Stefano Rastelli
Keyword(s):  
Rule Learning ◽  
Rote Learning ◽  
Association Measure ◽  
Grammatical Competence ◽  
Type Frequency

Abstract This article suggests a method to appraise L2 morphemes productivity in longitudinal learner data. Traditionally, morpheme productivity is believed to depend on type frequency and on proportion between inflected and uninflected lexemes. However, such measures cannot distinguish between rote-learning and rule-learning of target-like forms. In contrast, the association measure ΔP (delta pi) can quantify the extent to which a morpheme is contingent upon a limited number of lexemes. Decreasing contingency might parallel learners’ increasing awareness of asymmetrical morpheme-lexeme distribution in the input and this might be a cue of developing L2 grammatical competence beyond appearances. The paper presents the rationale and procedure for analyzing within-item variance – or the ‘intra-language’ – and illustrates a case-study concerning the perfective morpheme in L2 Italian.

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