(AUTORES EN ORDEN ALFABETICO) Finding Most Probable Worlds of Probabilistic Logic Programs

SAMIR KHULLER; MARIA VANINA MARTINEZ; DANA NAU; GERARDO I. SIMARI; AMY SLIVA; V.S. SUBRAHMANIAN

Scalable Uncertainty Management (SUM) - 1st International Conference (LNCS)

SPRINGER

Lugar: Berlin; Año: 2007; p. 45 - 59

Probabilistic logic programs have primarily studied the problem of entailment of probabilistic atoms. However, there are some interesting applications where we are interested in finding a possible world that is most probable. Our first result shows that the problem of computing such "maximally probable worlds" (MPW) is intractable. We subsequently show that we can often greatly reduce the size of the linear program used in past work (by Ng and Subrahmanian) and yet solve the problem exactly. However, the intractability results still make computational efficiency quite impossible. We therefore also develop several heuristics to solve the MPW problem and report extensive experimental results on the accuracy and efficiency of such heuristics.