Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T07:10:58.037Z Has data issue: false hasContentIssue false

The PITA system: Tabling and answer subsumption for reasoning under uncertainty

Published online by Cambridge University Press:  06 July 2011

FABRIZIO RIGUZZI
Affiliation:
ENDIF – University of Ferrara Via Saragat 1, I-44122 Ferrara, Italy (e-mail: fabrizio.riguzzi@unife.it)
TERRANCE SWIFT
Affiliation:
CENTRIA – Universidade Nova de Lisboa (e-mail: tswift@cs.suysb.edu)

Abstract

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.

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.

Type
Regular Papers
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baral, C., Gelfond, M. and Rushton, J. N. 2009. Probabilistic reasoning with answer sets. Theor. Pract. Log. Prog. 9 (1), 57144.CrossRefGoogle Scholar
Bauters, L., Schockaert, S., De Cock, M. and Vermeir, D. 2010. Possibilistic answer set programming revisited. In Conference on Uncertainty in Artificial Intelligence. AUAI Press.Google Scholar
Bistarelli, S., Montanari, U., Rossi, F. and Santini, F. 2007. Modelling multicast QoS routing by using best-tree search in and-or graphs and soft constraint logic programming. Electr. Notes Theor. Comput. Sci. 190 (3), 111127.CrossRefGoogle Scholar
Bistarelli, S. and Rossi, F. 2001. Semiring-based contstraint logic programming: syntax and semantics. ACM Trans. Program. Lang. Syst. 23 (1), 129.CrossRefGoogle Scholar
Christiansen, H. and Gallagher, J. P. 2009. Non-discriminating arguments and their uses. In International Conference on Logic Programming. LNCS, vol. 5649. Springer, 5569.CrossRefGoogle Scholar
Dantsin, E. 1991. Probabilistic logic programs and their semantics. In Russian Conference on Logic Programming. LNCS, vol. 592. Springer, 152164.Google Scholar
De Raedt, L., Demoen, B., Fierens, D., Gutmann, B., Janssens, G., Kimmig, A., Landwehr, N., Mantadelis, T., Meert, W., Rocha, R., Santos Costa, V., Thon, I. and Vennekens, J. 2008. Towards digesting the alphabet-soup of statistical relational learning. In NIPS2008 Workshop on Probabilistic Programming, 13 December 2008, Whistler, Canada.Google Scholar
De Raedt, L., Kimmig, A. and Toivonen, H. 2007. ProbLog: A probabilistic Prolog and its application in link discovery. In Internation Joint Conference on Artificial Intelligence, IJCAI, 2462–2467.Google Scholar
Dubois, D., Lang, J. and Prade, H. 1991. Towards possibilistic logic programming. In International Conference on Logic Programming. MIT Press, 581595.Google Scholar
Dubois, D., Lang, J. and Prade, H. 1994. Possibilistic logic. In Handbook of logic in artificial intelligence and logic programming, vol. 3, Gabbay, D. M., Hogger, C. J. and Robinson, J. A., Eds. Oxford University, 439514.CrossRefGoogle Scholar
Dubois, D. and Prade, H. 2004. Possibilistic logic: a retrospective and prospective view. Fuzzy Sets System 144 (1), 323.CrossRefGoogle Scholar
Kersting, K. and De Raedt, L. 2000. Bayesian logic programs. In Inductive Logic Programming, CEUR Workshop Proceedings. Sun SITE Central Europe.CrossRefGoogle Scholar
Kimmig, A., Costa, V. S., Rocha, R., Demoen, B. and Raedt, L. D. 2008. On the efficient execution of problog programs. In International Conference on Logic Programming. LNCS, vol. 5366. Springer, 175189.CrossRefGoogle Scholar
Meert, W., Struyf, J. and Blockeel, H. 2009. CP-Logic theory inference with contextual variable elimination and comparison to BDD based inference methods. In International Conference on Inductive Logic Programming. LNCS, vol. 5989. Springer, 96109.Google Scholar
Nicolas, P., Garcia, L., Stéphan, I. and Lefèvre, C. 2006. Possibilistic uncertainty handling for answer set programming. Ann. Math. Artif. Intell. 47 (1–2), 139181.CrossRefGoogle Scholar
Nieves, J. C., Osorio, M. and Cortés, U. 2007. Semantics for possibilistic disjunctive programs. In International Conference on Logic Programming and Nonmonotonic Reasoning. LNCS, vol. 4483. Springer, 315320.CrossRefGoogle Scholar
Osorio, M. and Nieves, J. C. 2009. Possibilistic well-founded semantics. In Mexican International Conference on Artificial Intelligence. LNCS, vol. 5845. Springer, 1526.Google Scholar
Poole, D. 1993. Logic programming, abduction and probability — a top-down anytime algorithm for estimating prior and posterior probabilities. New Gener. Comput. 11 (3), 377400.CrossRefGoogle Scholar
Poole, D. 1997. The Independent Choice Logic for modelling multiple agents under uncertainty. Artif. Intell. 94 (1–2), 756.CrossRefGoogle Scholar
Poole, D. 2000. Abducing through negation as failure: stable models within the independent choice logic. J. Log. Program. 44 (1–3), 535.CrossRefGoogle Scholar
Riguzzi, F. 2007. A top down interpreter for LPAD and CP-logic. In Congress of the Italian Association for Artificial Intelligence. LNAI, vol. 4733. Springer, 109120.Google Scholar
Riguzzi, F. 2008. Inference with logic programs with annotated disjunctions under the well founded semantics. In International Conference on Logic Programming. LNCS, vol. 5366. Springer, 667771.CrossRefGoogle Scholar
Riguzzi, F. 2009. Extended semantics and inference for the Independent Choice Logic. Logic J. IGPL 17 (6), 589629.CrossRefGoogle Scholar
Riguzzi, F. 2010. SLGAD resolution for inference on Logic Programs with Annotated Disjunctions. Fundam. Inform. 102 (3–4), 429466.CrossRefGoogle Scholar
Riguzzi, F. and Swift, T. 2010a. An extended semantics for logic programs with annotated disjunctions and its efficient implementation. In Italian Conference on Computational Logic. CEUR Workshop Proceedings, vol. 598. Sun SITE Central Europe.Google Scholar
Riguzzi, F. and Swift, T. 2010b. Tabling and Answer Subsumption for Reasoning on Logic Programs with Annotated Disjunctions. In Technical Communications of the International Conference on Logic Programming. LIPIcs, vol. 7. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 162–171.Google Scholar
Santos Costa, V., Page, D., Qazi, M. and Cussens, J. 2003. CLP(): Constraint logic programming for probabilistic knowledge. In Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann.Google Scholar
Sato, T. 1995. A statistical learning method for logic programs with distribution semantics. In International Conference on Logic Programming. MIT Press, 715729.CrossRefGoogle Scholar
Sato, T., Zhou, N.-F., Kameya, Y. and Izumi, Y. 2010. PRISM Users Manual (Version 2.0). http://sato-www.cs.titech.ac.jp/prism/download/prism20.pdf.Google Scholar
Swift, T. 1999. Tabling for non-monotonic programming. Ann. Math. Artif. Intell. 25 (3–4), 201240.CrossRefGoogle Scholar
Vennekens, J. and Verbaeten, S. 2003. Logic programs with annotated disjunctions. Tech. Rep. CW386, K. U. Leuven.CrossRefGoogle Scholar
Vennekens, J., Verbaeten, S. and Bruynooghe, M. 2004. Logic programs with annotated disjunctions. In International Conference on Logic Programming. LNCS, vol. 3131. Springer, 195209.CrossRefGoogle Scholar
Zhou, N.-F. 2011. The language features and architecture of B-Prolog. CoRR abs/1103.0812.Google Scholar