Learning the Structure of Related Tasks

We consider the problem of learning Bayes Net structures for related tasks. We present a formalism for learning related Bayes Net structures that takes advantage of the similarity between tasks by biasing toward learning similar structures for each task. Heuristic search is used to find a high scoring set of structures (one for each task), where the score for a set of structures is computed in a principled way. Experiments on synthetic problems generated from the ALARM and INSURANCE networks show that learning the structures for related tasks using the proposed method yields better results than learning the structures independently.