Communications of the ACM
The mathematics of inheritance systems
The mathematics of inheritance systems
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Model-preference default theories
Artificial Intelligence
Defaults and probabilities: extensions and coherence
Proceedings of the first international conference on Principles of knowledge representation and reasoning
What the lottery paradox tells us about default reasoning
Proceedings of the first international conference on Principles of knowledge representation and reasoning
On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Tractable default reasoning
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
On learning visual concepts and DNF formulae
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Causal default reasoning: principles and algorithms
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On learning Read-k-Satisfy-j DNF
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Circuits of the mind
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning to reason with a restricted view
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
Reasoning with Incomplete Information
Reasoning with Incomplete Information
Machine Learning
Machine Learning
Machine Learning
Exact learning via the Monotone theory
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
On the hardness of approximate reasoning
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Journal of the ACM (JACM)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Machine Learning
Learning to Reason with a Restricted View
Machine Learning
A neuroidal architecture for cognitive computation
Journal of the ACM (JACM)
Learning cost-sensitive active classifiers
Artificial Intelligence
ECML '02 Proceedings of the 13th European Conference on Machine Learning
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Logical analysis of binary data with missing bits
Artificial Intelligence
A connectionist framework for reasoning: reasoning with examples
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Implicit learning of common sense for reasoning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We suggest a new approach for the study of the non monotonicity of human commonsense reasoning. The two main premises that underlie this work are that commonsense reasoning is an inductive phenomenon and that missing information in the interaction of the agent with the environment may be as informative for future interactions as observed information. This intuition is normalized and the problem of reasoning from incomplete information is presented as a problem of learning attribute functions over a generalized domain. We consider examples that illustrate various aspects of the non monotonic reasoning phenomena which have been used over the years as bench marks for various formalisms and translate them into Learning to Reason problems. We demonstrate that these have concise representations over the generalized domain and prove that these representations can be learned efficiently. The framework developed suggests an operational approach to studying reasoning that is nevertheless rigorous and amenable to analysis. We show that this approach efficiently supports reasoning with incomplete information and at the same lime matches our expectations of plausible patterns of reasoning in cases where other theories do not. This work continues previous works in the Learning to Reason framework and supports the thesis that in order to develop a computational account for commonsense reasoning one should study the phenomena of learning and reasoning together.