Prolog programming for artificial intelligence
Prolog programming for artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An analysis of first-order logics of probability
Artificial Intelligence
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Relational rule induction with CPROGO14.4: a tutorial introductuon
Relational Data Mining
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning statistical models from relational data
Learning statistical models from relational data
ACM SIGKDD Explorations Newsletter
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
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Probabilistic Logic Models (PLMs) are efficient frameworks that combine the expressive power of first-order logic as knowledge representation and the capability to model uncertainty with probabilities. Stochastic Logic Programs (SLPs) and Statistical Relational Models (SRMs), which are considered as domain frequency approaches, and on the other hand Bayesian Logic Programs (BLPs) and Probabilistic Relational Models (PRMs) (possible worlds approaches), are promising PLMs in the categories. This paper is aimed at comparing the relative expressive power of these frameworks and developing translations between them based on a behavioral comparison of their semantics and probability computation. We identify that SLPs augmented with combining functions (namely extended SLPs) and BLPs can encode equivalent probability distributions, and we show how BLPs can define the same semantics as complete, range-restricted SLPs. We further demonstrate that BLPs (resp. SLPs) can encode the relational semantics of PRMs (resp. SRMs). Whenever applicable, we provide inter-translation algorithms, present their soundness and give worked examples.