[1][3] If for any choice of truth values for the probabilistic facts, the resulting logic program is stratified, it has a unique minimal Herbrand model which can be seen as the unique interpretation associated with that choice of truth values.
[1] The stable model semantics underlying answer set programming gives meaning to unstratified programs by allocating potentially more than one answer set to every truth value assignment of the probabilistic facts.
[4][5] The probabilistic logic programming language P-Log resolves this by dividing the probability mass equally between the answer sets, following the principle of indifference.
Its lower probability bound is defined by only considering those truth value assignments of the probabilistic facts for which the query is true in every answer set of the resulting program (cautious reasoning); its upper probability bound is defined by considering those assignments for which the query is true in some answer set (brave reasoning).
The compilation methods differ in the compactness of the target language and the class of queries and transformations that they support in polynomial time.
[2] As of 3 February 2024, this article is derived in whole or in part from Riguzzi, Fabrizio; Bellodi, Elena; Zese, Riccardo (2014).
The copyright holder has licensed the content in a manner that permits reuse under CC BY-SA 3.0 and GFDL.