5 research outputs found
TP-Compilation for inference in probabilistic logic programs
We propose TP -compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning. TP -compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations. Typically, these transformations include conversion of the ground program into an equivalent propositional formula and compilation of this formula into a more tractable target representation for weighted model counting. An empirical evaluation shows that TP -compilation effectively handles larger instances of complex or cyclic real-world problems than current sequential approaches, both for exact and anytime approximate inference. Furthermore, we show that TP -compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model
Valueâbased potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models
This study was jointly supported by the Spanish Ministry of Education and Science under projects PID2019-106758GB-C31 and TIN2016-77902-C3-2-P, and the European Regional Development Fund (FEDER). Funding for open access charge from Universidad de Granada/CBUA.When dealing with complex models (i.e., models with
many variables, a high degree of dependency between
variables, or many states per variable), the efficient representation
of quantitative information in probabilistic
graphical models (PGMs) is a challenging task. To address
this problem, this study introduces several new structures,
aptly named valueâbased potentials (VBPs), which are
based exclusively on the values. VBPs leverage repeated
values to reduce memory requirements. In the present
paper, they are compared with some common structures,
like standard tables or unidimensional arrays, and probability
trees (PT). Like VBPs, PTs are designed to reduce
the memory space, but this is achieved only if value repetitions
correspond to contextâspecific independence
patterns (i.e., repeated values are related to consecutive
indices or configurations). VBPs are devised to overcome
this limitation. The goal of this study is to analyze the
properties of VBPs. We provide a theoretical analysis of
VBPs and use them to encode the quantitative information
of a set of wellâknown Bayesian networks, measuring
the access time to their content and the computational
time required to perform some inference tasks.Spanish Government PID2019-106758GB-C31
TIN2016-77902-C3-2-PEuropean Commissio