aasasp-llncs.aux
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\newacro{BK}[BK]{background knowledge}
\newacro{ASP}[ASP]{answer set programming}
\newacro{NP}[NP]{normal program}
\newacro{DS}[DS]{distribution semantics}
\newacro{PF}[PF]{probabilistic fact}
\newacro{TC}[TC]{total choice}
\newacro{SM}[SM]{stable model}
\newacro{SC}[SC]{stable core}
\newacro{KL}[KL]{Kullback-Leibler}
\newacro{SBF}[SBF]{Simple But Fruitful}
\newacro{RSL}[RSL]{Random Set of Literals}
\newacro{RCE}[RCE]{Random Consistent Event}
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\newlabel{fig:running.example}{{1}{5}{Some events related to the \aclp {SM} of \cref {running.example}. The circle nodes are \aclp {TC} and shaded nodes are \aclp {SM}. Solid lines represent relations with the \acp {SM} and dashed lines relations between other events. The set of events contained in all \aclp {SM}, denoted by $\emptyevent $, is empty in this example}{figure.1.1}{}}
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\newlabel{fig:running.example.classes}{{2}{6}{Classes (of consistent events) related to the \aclp {SM} of \cref {running.example} are defined through intersections and inclusions. In this picture we can see, for example, the classes $\set {\co {c}ab, ab, b}$ and $\set {a, abc}$. Different fillings correspond to different classes and, as before, the circle nodes are \aclp {TC} and shaded nodes are \aclp {SM}. Notice that $bc$ is not in a ``filled'' area}{figure.1.2}{}}
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\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces \emph {Experiment 1.} Results from an experiment where $n=1000$ samples where generated following the \emph {Model+Noise} procedure with parameters $\alpha = 0.1, \beta = 0.3, \gamma = 0.2$. The \emph {empirical} distribution is represented by the random variable $S$ while the \emph {prior}, as before, is denoted by $E$.}}{12}{table.1.1}\protected@file@percent }
\newlabel{tab:sbf.example}{{1}{12}{\emph {Experiment 1.} Results from an experiment where $n=1000$ samples where generated following the \emph {Model+Noise} procedure with parameters $\alpha = 0.1, \beta = 0.3, \gamma = 0.2$. The \emph {empirical} distribution is represented by the random variable $S$ while the \emph {prior}, as before, is denoted by $E$}{table.1.1}{}}
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\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces \emph {Experiments 2 and 3.} Results from experiments, each with $n=1000$ samples generated following the \emph {Model+Noise} procedure, with parameters $\alpha = 0.1, \beta = 0.3, \gamma = 0.8$ (Experiment 2) and $\gamma =0.5$ (Experiment 3). Empirical distributions are represented by the random variables $S_{0.8}$ and $S_{0.5}$ respectively. Data from experience \cref {tab:sbf.example} is also included, and denoted by $S_{0.2}$, to provide reference.}}{13}{table.1.2}\protected@file@percent }
\newlabel{tab:sbf.examples.2.3}{{2}{13}{\emph {Experiments 2 and 3.} Results from experiments, each with $n=1000$ samples generated following the \emph {Model+Noise} procedure, with parameters $\alpha = 0.1, \beta = 0.3, \gamma = 0.8$ (Experiment 2) and $\gamma =0.5$ (Experiment 3). Empirical distributions are represented by the random variables $S_{0.8}$ and $S_{0.5}$ respectively. Data from experience \cref {tab:sbf.example} is also included, and denoted by $S_{0.2}$, to provide reference}{table.1.2}{}}
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