Speeding Up Inference for Probabilistic Logic Programs

  title={Speeding Up Inference for Probabilistic Logic Programs},
  author={Fabrizio Riguzzi},
  journal={Comput. J.},
Probabilistic Logic Programming (PLP) allows to represent domains containing many entities connected by uncertain relations and has many applications in particular in Machine Learning. PITA is a PLP algorithm for computing the probability of queries that exploits tabling, answer subsumption and Binary Decision Diagrams (BDDs). PITA does not impose any restriction on the programs. Other algorithms, such as PRISM, reduce computation time by imposing restrictions on the program, namely that… CONTINUE READING
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