Probabilistic Answer Set Programming (PASP) is a powerful formalism that allows to model uncertain scenarios with answer set programs. One of the possible semantics for PASP is the credal semantics, where a query is associated with a probability interval rather than a sharp probability value. In this paper, we extend the learning from interpretations task, usually considered for Probabilistic Logic Programming, to PASP: the goal is, given a set of (partial) interpretations, to learn the parameters of a PASP program such that the product of the lower bounds of the probability intervals of the interpretations is maximized. Experimental results show that the execution time of the algorithm is heavily dependent on the number of parameters rather than on the number of interpretations.

Learning the Parameters of Probabilistic Answer Set Programs

Azzolini D.
Primo
;
Bellodi E.;Riguzzi F.
2024

Abstract

Probabilistic Answer Set Programming (PASP) is a powerful formalism that allows to model uncertain scenarios with answer set programs. One of the possible semantics for PASP is the credal semantics, where a query is associated with a probability interval rather than a sharp probability value. In this paper, we extend the learning from interpretations task, usually considered for Probabilistic Logic Programming, to PASP: the goal is, given a set of (partial) interpretations, to learn the parameters of a PASP program such that the product of the lower bounds of the probability intervals of the interpretations is maximized. Experimental results show that the execution time of the algorithm is heavily dependent on the number of parameters rather than on the number of interpretations.
2024
9783031556296
9783031556302
Parameter Learning
Probabilistic Answer Set Programming
Statistical Relational Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2545030
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