Communications of the ACM
An introduction to computational learning theory
An introduction to computational learning theory
A Markovian extension of Valiant's learning model
Information and Computation
Journal of Computer and System Sciences
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Machine Learning
Machine Learning
Machine Learning
Some Alternative Formulations of the Event Calculus
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Ceteris Paribus preference elicitation with predictive guarantees
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Partial observability and learnability
Artificial Intelligence
Modular-E and the role of elaboration tolerance in solving the qualification problem
Artificial Intelligence
Inducing causal laws by regular inference
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Induction of the indirect effects of actions by monotonic methods
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Evolvability via the Fourier transform
Theoretical Computer Science
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The ability to predict, or at least recognize, the state of the world that an action brings about, is a central feature of autonomous agents. We propose, herein, a formal framework within which we investigate whether this ability can be autonomously learned. The framework makes explicit certain premises that we contend are central in such a learning task: (i) slow sensors may prevent the sensing of an action's direct effects during learning; (ii) predictions need to be made reliably in future and novel situations. We initiate in this work a thorough investigation of the conditions under which learning is or is not feasible. Despite the very strong negative learnability results that we obtain, we also identify interesting special cases where learning is feasible and useful.