Abstract: One of the difficulties that self-directed learners face on their learning process is choosing the right learning resources. One of the goals of adaptive educational syst...
We present two Bayesian algorithms CD-B and CD-H for discovering unconfounded cause and effect relationships from observational data without assuming causal sufficiency which prec...
Subramani Mani, Constantin F. Aliferis, Alexander ...
The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with Conditional Gaussian Network (CGN). The inter...
Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an ...
Abstract. This paper presents a technique with which instances of argument structures in the Carneades model can be given a probabilistic semantics by translating them into Bayesia...
Matthias Grabmair, Thomas F. Gordon, Douglas Walto...
A Skilligent robot must be able to learn skills autonomously to accomplish a task. "Skilligence" is the capacity of the robot to control behaviors reasonably, based on th...
Combining classifier methods have shown their effectiveness in a number of applications. Nonetheless, using simultaneously multiple classifiers may result in some cases in a reduc...
Claudio De Stefano, Francesco Fontanella, Alessand...
: Previously we have proposed a theoretical framework, called BayesOWL, to model uncertainty in semantic web ontologies based on Bayesian networks. In particular, we have developed...
Time series are found widely in engineering and science. We study multiagent forecasting in time series, drawing from literature on time series, graphical models, and multiagent s...
Whenever a dataset has multiple discrete target variables, we want our algorithms to consider not only the variables themselves, but also the interdependencies between them. We pro...
Wouter Duivesteijn, Arno J. Knobbe, Ad Feelders, M...