Abstract
We present an algorithm that learns acyclic propositional probabilistic logic programs from complete data, by adapting techniques from Bayesian network learning. Specifically, we focus on score-based learning and on exact maximum likelihood computations. Our main contribution is to show that by restricting any rule body to contain at most two literals, most needed optimization steps can be solved exactly. We describe experiments indicating that our techniques do produce accurate models from data with reduced numbers of parameters.
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Acknowledgement
The first author is supported by a scholarship from Toshiba Corporation. The second and third authors are partially supported by CNPq. This work was partly supported by the São Paulo Research Foundation (FAPESP) grant 2016/01055-1 and the CNPq grants 303920/2016-5 and 420669/2016-7; also by São Paulo Research Foundation (FAPESP) grant 2016/18841-0 and CNPq grant 308433/2014-9; finally by FAPESP 2015/21880-4.
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Otte Vieira de Faria, F.H., Cozman, F.G., Mauá, D.D. (2017). Closed-Form Solutions in Learning Probabilistic Logic Programs by Exact Score Maximization. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_9
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