Communications of the ACM
Readings in nonmonotonic reasoning
Readings in nonmonotonic reasoning
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computational limitations on learning from examples
Journal of the ACM (JACM)
A general lower bound on the number of examples needed for learning
Information and Computation
Unified theories of cognition
From on-line to batch learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Learning in the presence of malicious errors
SIAM Journal on Computing
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
A formal model of hierarchical concept learning
Information and Computation
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Circuits of the mind
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Artificial Intelligence
Machine Learning
The Architecture of Cognition
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
Machine Learning
Relational Learning for NLP using Linear Threshold Elements
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning noisy perceptrons by a perceptron in polynomial time
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
A polynomial-time algorithm for learning noisy linear threshold functions
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Human Problem Solving
Learning to reason the non monotonic case
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Neural Computation
Experiments with Projection Learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Theoretical Computer Science
Projective DNF formulae and their revision
Discrete Applied Mathematics
Learning to assign degrees of belief in relational domains
Machine Learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A connectionist cognitive model for temporal synchronisation and learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
On attribute efficient and non-adaptive learning of parities and DNF expressions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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An architecture is described for designing systems that acquire and ma nipulate 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 this 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 modular fashion.