Bayesian Logic Programs

TL;DR

Bayesian logic programs extend Bayesian networks by integrating them with logic programming, enabling better representation of objects and relations while maintaining probabilistic reasoning capabilities.

Contribution

The paper introduces Bayesian logic programs, combining Bayesian networks with logic programming to address limitations in representing objects and relations.

Findings

Bayesian logic programs effectively model complex relational structures.

They unify probabilistic reasoning with logical representations.

The approach generalizes both Bayesian networks and logic programs.

Abstract

Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developed