Abstract This paper proposes an approach for reducing the computational complexity of a model-predictive-control strategy for discrete-time hybrid systems with discrete inputs only...
Bostjan Potocnik, Gasper Music, Igor Skrjanc, Boru...
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to de...
Abstract. Logical Bayesian Networks (LBNs) have recently been introduced as another language for knowledge based model construction of Bayesian networks, besides existing languages...
Daan Fierens, Hendrik Blockeel, Maurice Bruynooghe...
Continuing our research in explanation-oriented language design, we present a domain-specific visual language for explaining probabilistic reasoning. Programs in this language, c...
We present an automatic analyzer for measuring information flow within software systems. In this paper, we quantify leakage in terms of information theory and incorporate this comp...