Relativized options combine model minimization methods and a hierarchical reinforcement learning framework to derive compact reduced representations of a related family of tasks. ...
— In this paper, we studied how a mobile robot equipped with a 3D laser scanner can start from primitive behaviors and learn to use them to achieve goal-directed behaviors. For t...
Mehmet Remzi Dogar, Maya Cakmak, Emre Ugur, Erol S...
Many events in news articles don't include time arguments. This paper describes two methods, one based on rules and the other based on statistical learning, to predict the un...
Framing in the presence of data abstraction is a challenging and important problem in the verification of object-oriented programs [LLM07]. The dynamic frames approach is a promisi...
Jan Smans, Bart Jacobs, Frank Piessens, Wolfram Sc...
We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation...