scholarly journals Margin-based first-order rule learning

2007 ◽  
Vol 70 (2-3) ◽  
pp. 189-206 ◽  
Author(s):  
Ulrich Rückert ◽  
Stefan Kramer
Keyword(s):  
1978 ◽  
Vol 22 (2) ◽  
pp. 161-178 ◽  
Author(s):  
Gregory C. R. Yates ◽  
Shirley M. Yates

The topic of imitative learning, or social modelling, has stimulated a large amount of empirical research in recent years. This article reviews this research from the perspective of social learning theory which emphasizes the human capacity for higher-order rule learning to occur through modelling exposure. Variables relevant to observational learning are distinguished from the variables more directly relevant to imitative performance. Educational implications of these findings are discussed, particularly through research into vicarious reinforcement, teacher modelling and peer modelling.


1967 ◽  
Vol 21 (3) ◽  
pp. 921-927
Author(s):  
John A. Robinson

The discovery and use of transformational rules as well as subsequent interference among such rules was investigated. 24 single-solution trigrams were permuted from their respective solution-words by a uniform letter-order rule (LOR) and assigned to one of 2 lists. LORs were either the same for both lists or different. Two control groups were included to assess the effects of practice on anagram solution and of prior rule-learning experience. Ss were simply asked to discover and say aloud the solution words. There was no suggestion that rules could be formulated. Comparisons among conditions using mean median solution time for successive blocks of list-items indicated that (a) practice has no effect on solution time with nonrule materials, (b) encoding rule learning does occur, and (c) when rules are changed (List I to List II) solution time increases significantly, i.e., encoding rule interference results.


1996 ◽  
Vol 12 (4) ◽  
pp. 523-540 ◽  
Author(s):  
M. O. Cordier ◽  
S. Loiseau

Neuroscience ◽  
2017 ◽  
Vol 345 ◽  
pp. 99-109 ◽  
Author(s):  
P.E. Dickson ◽  
J. Cairns ◽  
D. Goldowitz ◽  
G. Mittleman

Author(s):  
MAI XU ◽  
MARIA PETROU ◽  
JIANHUA LU

In this paper, we propose a novel logic-rule learning approach for the Tower of Knowledge (ToK) architecture, based on Markov logic networks, for scene interpretation. This approach is in the spirit of the recently proposed Markov logic networks for machine learning. Its purpose is to learn the soft-constraint logic rules for labeling the components of a scene. In our approach, FOIL (First Order Inductive Learner) is applied to learn the logic rules for MLN and then gradient ascent search is utilized to compute weights attached to each rule for softening the rules. This approach also benefits from the architecture of ToK, in reasoning whether a component in a scene has the right characteristics in order to fulfil the functions a label implies, from the logic point of view. One significant advantage of the proposed approach, rather than the previous versions of ToK, is its automatic logic learning capability such that the manual insertion of logic rules is not necessary. Experiments of labeling the identified components in buildings, for building scene interpretation, illustrate the promise of this approach.


2020 ◽  
Author(s):  
Romain Quentin ◽  
Lison Fanuel ◽  
Mariann Kiss ◽  
Marine Vernet ◽  
Teodóra Vékony ◽  
...  

AbstractKnowing when the brain learns is crucial for both the comprehension of memory formation and consolidation, and for developing new training and neurorehabilitation strategies in healthy and patient populations. Recently, a rapid form of offline learning developing during short rest periods has been shown to account for most of procedural learning, leading to the hypothesis that the brain mainly learns during rest between practice periods. Nonetheless, procedural learning has several subcomponents not disentangled in previous studies investigating learning dynamics, such as acquiring the statistical regularities of the task, or else the high-order rules that regulate its organization. Here, we analyzed 506 behavioral sessions of implicit visuomotor deterministic and probabilistic sequence learning tasks, allowing the distinction between general skill learning, statistical learning and high-order rule learning. Our results show that the temporal dynamics of apparently simultaneous learning processes differ. While general skill and high-order rule learning are acquired offline, statistical learning is evidenced online. These findings open new avenues on the short-scale temporal dynamics of learning and memory consolidation and reveal a fundamental distinction between statistical and high-order rule learning, the former benefiting from online evidence accumulation and the latter requiring short rest periods for rapid consolidation.


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