Probabilistic inference will be of special importance when one needs to know how much we can say with what all we know given new observations. Bayesian Network is a graphical prob...
This paper is concerned with a class of algorithms that perform exhaustive search on propositional knowledge bases. We show that each of these algorithms defines and generates a ...
In this paper, we propose a scheme for evaluating learners’ knowledge level on concept mapping tasks. The assessment process focuses on the propositions presented on learner’s...
Evangelia Gouli, Agoritsa Gogoulou, Kyparisia A. P...
In traditional classification setting, training data are represented as a single table, where each row corresponds to an example and each column to a predictor variable or the targ...
In this paper, we investigate the extent to which knowledge compilation can be used to improve model checking and inference from propositional weighted bases. We first focus on th...