Reinforcing the Object-Oriented Aspect of Probabilistic Relational Models

Abstract

Representing uncertainty in knowledge is a common issue in Artificial Intelligence. Bayesian Networks have been one of the main models used in this field of research. The simplicity of their specification is one of the reason for their success, both in industrial and in theoretical domains. The widespread use of Bayesian Networks brings new challenges in the design and use of largescale systems, where this very simplicity causes a lack of expressiveness and scalability. To fill this gap, an increasing number of languages emerged as extensions of Bayesian Networks with many approaches: first-order logic, object-oriented, entity-relation, and so on. In this paper we focus on Probabilistic Relational Models, an object-oriented extension. However, Probabilistic Relational Models do not fully exploit the object-oriented paradigm, in particular they lack class inheritance. Using Object-Oriented Bayesian Networks as a basis, we propose to lightly extend PRMs framework resulting in stronger object-oriented aspects in probabilistic models. Probabilistic graphical models (Koller and Friedman, 2009) are a general purpose framework for dealing with uncertainty. Their applications to many different domains has stimulated an uninterrupted process of creation of new frameworks based on probability theory. Bayesian Networks (Pearl, 1988) are among the most popular framework for uncertainty in AI. In recent years, the Statistical Learning community has actively proposed new probabilistic frameworks, closing the gap between first-order logic and probability theory (Getoor and Taskar, 2007). New models such as Object-Oriented Bayesian