Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
In the past decade, moving horizon estimation (MHE) has emerged as a powerful technique for estimating the state of a dynamical system in the presence of nonlinearities and disturb...
Angelo Alessandri, Marco Baglietto, Giorgio Battis...
The Valued Constraint Satisfaction Problem (VCSP) is a general framework encompassing many optimisation problems. We discuss precisely what it means for a problem to be modelled in...
Recent advances in scene understanding and related tasks
have highlighted the importance of using regions to reason
about high-level scene structure. Typically, the regions are
...
In this work, our objective is to heuristically discover a simplified form of functional dependencies between variables called weak dependencies. Once discovered, these relations...