A logical framework for default reasoning
Artificial Intelligence
Induction from the general to the more general
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Propositional knowledge base revision and minimal change
Artificial Intelligence
An introduction to computational learning theory
An introduction to computational learning theory
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A knowledge-based framework for belief change part I: foundations
TARK '94 Proceedings of the 5th conference on Theoretical aspects of reasoning about knowledge
Epistemic Context, Defeasible Inference and Conversational Implicature
CONTEXT '99 Proceedings of the Second International and Interdisciplinary Conference on Modeling and Using Context
Convergency of Learning Process
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Context-sensitive text mining and belief revision for intelligent information retrieval on the web
Web Intelligence and Agent Systems
Bridges between dynamic doxastic and doxastic temporal logics
LOFT'08 Proceedings of the 8th international conference on Logic and the foundations of game and decision theory
Belief revision as a truth-tracking process
Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge
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Belief revision theory aims to describe how one should change one's beliefs when they are contradicted by newly input information. The guiding principle of belief revision theory is to change one's prior beliefs as little as possible in order to maintain consistency with the new information. Learning theory focuses, instead, on learning power: the ability to arrive at true beliefs in a wide range of possible environments. The goal of this paper is to bridge the two approaches by providing a learning theoretic analysis of the learning power of belief revision methods proposed by Spohn, Boutilier, Darwiche and Pearl, and others. The results indicate that learning power depends sharply on details of the methods. Hence, learning power can provide a well-motivated constraint on the design and implementation of concrete belief revision methods.