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MAP Inference for Probabilistic Logic Programming

Published online by Cambridge University Press:  21 September 2020

ELENA BELLODI ,
MARCO ALBERTI ,
FABRIZIO RIGUZZI Open the ORCID record for FABRIZIO RIGUZZI [Opens in a new window]  and
RICCARDO ZESE Open the ORCID record for RICCARDO ZESE [Opens in a new window]
ELENA BELLODI
Affiliation:
Dipartimento di Ingegneria – Università di Ferrara
MARCO ALBERTI
Affiliation:
Dipartimento di Matematica e Informatica – Università di Ferrara Via Saragat 1, 44122, Ferrara, Italy (e-mail: [email protected])
FABRIZIO RIGUZZI
Affiliation:
Dipartimento di Matematica e Informatica – Università di Ferrara Via Saragat 1, 44122, Ferrara, Italy (e-mail: [email protected])
RICCARDO ZESE
Affiliation:
Dipartimento di Ingegneria – Università di Ferrara
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Abstract

In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases.

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 5: 36th International Conference on Logic Programming Special Issue I , September 2020 , pp. 641 - 655
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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