Abstract
We present ๐พ๐๐ป๐ผโan online structure learner for Markov Logic Networks (MLNs) that exploits background knowledge axiomatization in order to constrain the space of possible structures. Many domains of interest are characterized by uncertainty and complex relational structure. MLNs is a state-of-the-art Statistical Relational Learning framework that can naturally be applied to domains governed by these characteristics. Learning MLNs from data is challenging, as their relational structure increases the complexity of the learning process. In addition, due to the dynamic nature of many real-world applications, it is desirable to incrementally learn or revise the modelโs structure and parameters. Experimental results are presented in activity recognition using a probabilistic variant of the Event Calculus (๐ผ๐ป๐ฝโ๐ด๐ฒ) as background knowledge and a benchmark dataset for video surveillance.
