The PITA system: tabling and answer subsumption for reasoning under uncertainty. Many real world domains require the representation of a measure of uncertainty. The most common such representation is probability, and the combination of probability with logic programs has given rise to the field of probabilistic logic programming (PLP), leading to languages such as the independent choice logic, logic programs with annotated disjunctions (LPADs), Problog, PRISM, and others. These languages share a similar distribution semantics, and methods have been devised to translate programs between these languages. The complexity of computing the probability of queries to these general PLP programs is very high due to the need to combine the probabilities of explanations that may not be exclusive. As one alternative, the PRISM system reduces the complexity of query answering by restricting the form of programs it can evaluate. As an entirely different alternative, possibilistic logic programs adopt a simpler metric of uncertainty than probability.par Each of these approaches -- general PLP, restricted PLP, and possibilistic logic programming -- can be useful in different domains depending on the form of uncertainty to be represented, on the form of programs needed to model problems, and on the scale of the problems to be solved. In this paper, we show how the PITA system, which originally supported the general PLP language of LPADs, can also efficiently support restricted PLP and possibilistic logic programs. PITA relies on tabling with answer subsumption and consists of a transformation along with an API for library functions that interface with answer subsumption. We show that, by adapting its transformation and library functions, PITA can be parameterized to PITA(IND, EXC) which supports the restricted PLP of PRISM, including optimizations that reduce non-discriminating arguments and the computation of Viterbi paths. Furthermore, we show PITA to be competitive with PRISM for complex queries to Hidden Markov Model examples, and sometimes much faster. We further show how PITA can be parameterized to PITA(COUNT) which computes the number of different explanations for a subgoal, and to PITA(POSS) which scalably implements possibilistic logic programming. PITA is a supported package in version 3.3 of XSB.

References in zbMATH (referenced in 22 articles , 2 standard articles )

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  1. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio; Lamma, Evelina: Learning hierarchical probabilistic logic programs (2021)
  2. Bellodi, Elena; Alberti, Marco; Riguzzi, Fabrizio; Zese, Riccardo: MAP inference for probabilistic logic programming (2020)
  3. Mantadelis, Theofrastos; Bistarelli, Stefano: Probabilistic abstract argumentation frameworks, a possible world view (2020)
  4. Srinivasan, Ashwin; Vig, Lovekesh; Shroff, Gautam: Constructing generative logical models for optimisation problems using domain knowledge (2020)
  5. Ghosh, Sarthak; Ramakrishnan, C. R.: Value of information in probabilistic logic programs (2019)
  6. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  7. Zese, Riccardo; Cota, Giuseppe; Lamma, Evelina; Bellodi, Elena; Riguzzi, Fabrizio: Probabilistic DL reasoning with pinpointing formulas: a Prolog-based approach (2019)
  8. Nampally, Arun; Zhang, Timothy; Ramakrishnan, C. R.: Constraint-based inference in probabilistic logic programs (2018)
  9. Schwitter, Rolf: Learning effect axioms via probabilistic logic programming (2018)
  10. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
  11. Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
  12. Nampally, Arun; Ramakrishnan, C. R.: Inference in probabilistic logic programs using lifted explanations (2016)
  13. Riguzzi, Fabrizio: The distribution semantics for normal programs with function symbols (2016)
  14. Vlasselaer, Jonas; Van den Broeck, Guy; Kimmig, Angelika; Meert, Wannes; De Raedt, Luc: (T_\mathcalP)-compilation for inference in probabilistic logic programs (2016)
  15. Bellodi, Elena; Riguzzi, Fabrizio: Structure learning of probabilistic logic programs by searching the clause space (2015)
  16. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  17. Di Mauro, Nicola; Bellodi, Elena; Riguzzi, Fabrizio: Bandit-based Monte-Carlo structure learning of probabilistic logic programs (2015)
  18. Sato, Taisuke; Kubota, Keiichi: Viterbi training in PRISM (2015)
  19. Van Ranst, Wiebe; Vennekens, Joost: An OpenCL implementation of a forward sampling algorithm for CP-logic (2015)
  20. Riguzzi, Fabrizio; Swift, Terrance: Well-definedness and efficient inference for probabilistic logic programming under the distribution semantics (2013)

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