A neuroidal architecture for cognitive computation

A neuroidal architecture for cognitive computation (2000)

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by Leslie G. Valiant

Venue:	Journal of the ACM
Citations:	32 - 4 self

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Datum	Value	Source
TITLE	A neuroidal architecture for cognitive computation	INFERENCE
AUTHOR NAME	Leslie G. Valiant	SVM HeaderParse 0.2
AUTHOR AFFIL	Harvard University, Cambridge	SVM HeaderParse 0.2
AUTHOR ADDR	Massachusetts	SVM HeaderParse 0.2
ABSTRACT	Abstract. An architecture is described for designing systems that acquire and manipulate large amounts of unsystematized, or so-called commonsense, knowledge. Its aim is to exploit to the full those aspects of computational learning that are known to offer powerful solutions in the acquisition and maintenance of robust knowledge bases. The architecture makes explicit the requirements on the basic computational tasks that are to be performed and is designed to make these computationally tractable even for very large databases. The main claims are that (i) the basic learning and deduction tasks are provably tractable and (ii) tractable learning offers viable approaches to a range of issues that have been previously identified as problematic for artificial intelligence systems that are programmed. Among the issues that learning offers to resolve are robustness to inconsistencies, robustness to incomplete information and resolving among alternatives. Attribute-efficient learning algorithms, which allow learning from few examples in large dimensional systems, are fundamental to the approach. Underpinning the overall architecture is a new principled approach to manipulating relations in learning systems. This approach, of independently quantified arguments, allows propositional learning algorithms to be applied systematically to learning relational concepts in polynomial time and in a modular fashion.	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE	Journal of the ACM	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	642--669	INFERENCE
VOLUME	47	INFERENCE
TECH	Technical Report TR-11-96	INFERENCE
CITATIONS	41 found	ParsCit 1.0