Many real world domains require the representation of a measure ofuncertainty. The most common such representation is probability, and thecombination of probability with logic programs has given rise to the field ofProbabilistic Logic Programming (PLP), leading to languages such as theIndependent Choice Logic, Logic Programs with Annotated Disjunctions (LPADs),Problog, PRISM and others. These languages share a similar distributionsemantics, and methods have been devised to translate programs between theselanguages. The complexity of computing the probability of queries to thesegeneral PLP programs is very high due to the need to combine the probabilitiesof explanations that may not be exclusive. As one alternative, the PRISM systemreduces the complexity of query answering by restricting the form of programsit can evaluate. As an entirely different alternative, Possibilistic LogicPrograms adopt a simpler metric of uncertainty than probability. Each of theseapproaches -- general PLP, restricted PLP, and Possibilistic Logic Programming-- can be useful in different domains depending on the form of uncertainty tobe represented, on the form of programs needed to model problems, and on thescale 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 alsoefficiently support restricted PLP and Possibilistic Logic Programs. PITArelies on tabling with answer subsumption and consists of a transformationalong with an API for library functions that interface with answer subsumption.
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