Paper Info
Title
Cognitive Meta-learning of Syntactically Inferred Concepts.
Abstract
This paper outlines a proposal for a two-level cognitive architecture reproducing the process of abstract thinking in human beings. The key idea is the use of a level devoted to the extraction of compact representation for basic concepts, with additional syntactic inference carried on at a meta-level, in order to provide generalization. Higher-level concepts are inferred according to a principle of simplicity, consistent with Kolmogorov complexity, and merged back into the lower level in order to widen the underlying knowledge base.
Year
DOI
Venue
2011
10.3233/978-1-60750-959-2-118
BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES 2011
Keywords
Field
DocType
Cognitive learning,Grammar Inference,Kolmogorov complexity
Kolmogorov complexity,Inference,Computer science,Natural language processing,Artificial intelligence,Knowledge base,Cognitive architecture,Cognition,Syntax
Conference
Volume
ISSN
Citations 
233
0922-6389
0
PageRank 
References 
Authors
0.34
1
3
Name
Order
Citations
PageRank
Salvatore Gaglio166088.41
Giuseppe Lo Re233841.26
Marco Ortolani320921.31