The probabilistic graphs framework models the uncertainty inherent in real-world domains by means of probabilistic edges whose value quantifies the likelihood of the edge existence or the strength of the link it represents. The goal of this paper is to provide a learning method to compute the most likely relationship between two nodes in a framework based on probabilistic graphs. In particular,...

Dynamic Bayesian networks (DBNs) are temporal probabilistic models for reasoning over time which are rapidly gaining popularity in modern Artificial Intelligence (AI) for planning. A number of Hidden Markov Model (HMM) representations of dynamic Bayesian networks with different characteristics have been developed. However, the varieties of DBNs have obviously opened up challenging problems of h...

A probabilistic variant of ATL∗ logic is proposed to work with multi-player games of incomplete information and synchronous perfect recall. The semantics of the logic is settled over probabilistic interpreted system and partially observed probabilistic concurrent game structure. While unexpectedly, the model checking problem is in general undecidable even for singlegroup fragment, we find a fra...

an electric arc furnace (eaf) is known as nonlinear and time variant load that causes power quality (pq) problems such as, current, voltage and current harmonics, voltage flicker, frequency changes in power system. one of the most important problems to study the eaf behavior is the choice of a suitable model for this load. hence, in this paper, a probabilistic three-phase model is proposed base...