Networked embedded systems such as wireless sensor networks are usually designed to be event-driven so that they are reactive and power efficient. Programming embedded systems wit...
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Abstract. This paper evaluates the power of a new scheme that generates search heuristics mechanically. This approach was presented and evaluated rst in the context of optimization...
Autonomy requires robustness. The use of unmanned (autonomous) vehicles is appealing for tasks which are dangerous or dull. However, increased reliance on autonomous robots increa...
Eliahu Khalastchi, Gal A. Kaminka, Meir Kalech, Ra...
Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...