TY - CONF
AU - Azzolini, Damiano
AU - Bellodi, Elena
AU - Riguzzi, Fabrizio
ED - Muggleton, Stephen H.
ED - Tamaddoni-Nezhad, Alireza
PY - 2024
DA - 2024//
TI - Learning the Parameters of Probabilistic Answer Set Programs
BT - Inductive Logic Programming
SP - 1
EP - 14
PB - Springer Nature Switzerland
CY - Cham
AB - 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.
SN - 978-3-031-55630-2
ID - 10.1007/978-3-031-55630-2_1
ER -