Probabilistic Logics in Expert Systems: Approaches, Implementations, and Applications

The handling of uncertain information is of crucial importance for the success of expert systems. This paper gives an overview on logic-based approaches to probabilistic reasoning and goes into more details about recent developments for relational, respectively first-order, probabilistic methods like Markov logic networks, and Bayesian logic programs. In particular, we will feature the maximum entropy approach as a powerful and elegant method that combines convenience with respect to knowledge representation with excellent inference properties. We briefly describe some systems for probabilistic reasoning, and go into more details on the KReator system as a versatile toolbox for probabilistic relational learning, modelling, and inference. Moreover, we will illustrate applications of probabilistic logics in various scenarios.