Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space ro...
To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have turned to self-adaptivity. Self-adaptive systems are capab...
Yuriy Brun, Giovanna Di Marzo Serugendo, Cristina ...
This paper is about Reinforcement Learning (RL) applied to online parameter tuning in Stochastic Local Search (SLS) methods. In particular a novel application of RL is considered i...
We present a visualization system allowing non-programmers to visualize, explore, and analyze unknown multivariate data by designing an appropriate glyph representation with minim...
Abstract— Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, they make restrictive assumptions that limit their applic...