We introduce a new execution model for orchestrating the interaction between the conventional processor and the reconfigurable compute unit in adaptive computer systems. We then ...
This paper presents an efficient method of learning motion control for autonomous animated characters. The method uses a non parametric learning approach which identifies non line...
We propose an opponent modeling approach for no-limit Texas hold-em poker that starts from a (learned) prior, i.e., general expectations about opponent behavior and learns a relat...
Marc J. V. Ponsen, Jan Ramon, Tom Croonenborghs, K...
This paper provides an algorithmic framework for learning statistical models involving directed spanning trees, or equivalently non-projective dependency structures. We show how p...
Terry Koo, Amir Globerson, Xavier Carreras, Michae...
In this paper I propose a model for situated information processing based on a set of layers, each related to a different processing phase and to a different context type. The mod...