1/* 2Model of the development of an epidemic or a pandemic. 3From 4E. Bellodi and F. Riguzzi. Expectation Maximization over binary decision 5diagrams for probabilistic logic programs. Intelligent Data Analysis, 617(2):343-363, 2013. 7*/ 8:- use_module(library(pita)). 9 10:- if(current_predicate(use_rendering/1)). 11:- use_rendering(c3). 12:- use_rendering(graphviz). 13:- use_rendering(table,[header(['Multivalued variable index','Rule index','Grounding substitution'])]). 14:- endif. 15 16:- pita. 17 18:- begin_lpad. 19 20epidemic : 0.6; pandemic : 0.3 :- flu(_), cold. 21% if somebody has the flu and the climate is cold, there is the possibility 22% that an epidemic arises with probability 0.6 and the possibility that a 23% pandemic arises with probability 0.3 24 25cold 0.7. 26% it is cold with probability 0.7 27 28flu(david). 29flu(robert). 30% david and robert have the flu for sure 31 32:- end_lpad.
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prob(epidemic,Prob)
. % what is the probability that an epidemic arises? % expected result 0.588 ?-prob(pandemic,Prob)
. % what is the probability that a pandemic arises? % expected result 0.357 ?-prob(epidemic,Prob)
,bar(Prob,C)
. % what is the probability that an epidemic arises? % expected result 0.588 ?-prob(pandemic,Prob)
,bar(Prob,C)
. % what is the probability that a pandemic arises? % expected result 0.357 ?-bdd_dot_string(epidemic,BDD,Var)
.?-
bdd_dot_string(pandemic,BDD,Var)
.*/