Foundations of a functional approach to knowledge representation.
Artificial Intelligence
Communications of the ACM
Artificial Intelligence
Making believers out of computers
Artificial Intelligence
NP is as easy as detecting unique solutions
Theoretical Computer Science
Learning in the presence of malicious errors
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Foundations of AI: the big issues
Artificial Intelligence
Logic and artificial intelligence
Artificial Intelligence
Intelligence without representation
Artificial Intelligence
On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Structure identification in relational data
Artificial Intelligence - Special volume on constraint-based reasoning
Tractable default reasoning
Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
Machine Learning - Special issue on computational learning theory
Circuits of the mind
Horn approximations of empirical data
Artificial Intelligence
Reasoning about knowledge
Exact learning Boolean functions via the monotone theory
Information and Computation
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning to reason with a restricted view
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
On the hardness of approximate reasoning
Artificial Intelligence
Off-line reasoning for on-line efficiency: knowledge bases
Artificial Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Artificial Intelligence
Tractable Reasoning in Artificial Intelligence
Tractable Reasoning in Artificial Intelligence
Machine Learning
Machine Learning
Machine Learning
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
Learning first order universal Horn expressions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Learning Function-Free Horn Expressions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Decomposable negation normal form
Journal of the ACM (JACM)
Remarks on computational learning theory
Annals of Mathematics and Artificial Intelligence
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
Learning in natural language: theory and algorithmic approaches
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Learning to assign degrees of belief in relational domains
Machine Learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Constructive induction: a version space-based approach
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A perspective on knowledge compilation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Learning to assign degrees of belief in relational domains
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Implicit learning of common sense for reasoning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
From machine learning to machine reasoning
Machine Learning
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We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning as an integral part of the inference process, and suggests that learning and reasoning should be studied together.The Learning to Reason framework combines the interfaces to the world used by known learning models with the reasoning task and a performance criterion suitable for it. In this framework, the intelligent agent is given access to its favorite learning interface, and is also given a grace period in with it can interact with this interface and construct a representation KB of the world W. The reasoning performance is measured only after this period, when the agent is presented with queries &agr; from some query language, relevant to the world, and has to answer whether W implies &agr;.The approach is meant to overcome the main computational difficulties in the traditional treatment of reasoning which stem from its separation from the “world”. Since the agent interacts with the world when construction its knowledge representation it can choose a representation that is useful for the task at hand. Moreover, we can now make explicit the dependence of the reasoning performance on the environment the agent interacts with.We show how previous results from learning theory and reasoning fit into this framwork and illustrate the usefulness of the Learning to Reason approach by exhibiting new results that are not possible in the traditional setting. First, we give Learning to Reason algorithms for classes of propositional languages for which there are no efficient reasoning algorithms, when represented as a traditional (formula-based) knowledge base. Second, we exhibit a Learning to Reason algorithm for a class of propositional languages that is not know to be learnable in the traditional sense.