Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform conve...
We illustrate a simple algorithm for approximating the medial axis of a 2D shape with smooth boundary from a sample of this boundary. The algorithm is compared to a more general a...
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
Techniques for computation on generalized diagrams are defined and the KM implications are explored. Descriptive Computing is presented and plan computation based on world models t...
We present a general framework for defining nonmonotonic systems based on the notion of preferred maximal consistent subsets of the premises. This framework subsumes David Poole...