131,322 research outputs found
“What if?” in Probabilistic Logic Programming
A ProbLog program is a logic program with facts that only hold with a specified probability. In this contribution, we extend this ProbLog language by the ability to answer “What if” queries. Intuitively, a ProbLog program defines a distribution by solving a system of equations in terms of mutually independent predefined Boolean random variables. In the theory of causality, Judea Pearl proposes a counterfactual reasoning for such systems of equations. Based on Pearl’s calculus, we provide a procedure for processing these counterfactual queries on ProbLog programs, together with a proof of correctness and a full implementation. Using the latter, we provide insights into the influence of different parameters on the scalability of inference. Finally, we also show that our approach is consistent with CP-logic, that is with the causal semantics for logic programs with annotated with disjunctions
"What if?" in Probabilistic Logic Programming
A ProbLog program is a logic program with facts that only hold with a
specified probability. In this contribution we extend this ProbLog language by
the ability to answer "What if" queries. Intuitively, a ProbLog program defines
a distribution by solving a system of equations in terms of mutually
independent predefined Boolean random variables. In the theory of causality,
Judea Pearl proposes a counterfactual reasoning for such systems of equations.
Based on Pearl's calculus, we provide a procedure for processing these
counterfactual queries on ProbLog programs, together with a proof of
correctness and a full implementation. Using the latter, we provide insights
into the influence of different parameters on the scalability of inference.
Finally, we also show that our approach is consistent with CP-logic, i.e. with
the causal semantics for logic programs with annotated with disjunctions
Lifted Variable Elimination for Probabilistic Logic Programming
Lifted inference has been proposed for various probabilistic logical
frameworks in order to compute the probability of queries in a time that
depends on the size of the domains of the random variables rather than the
number of instances. Even if various authors have underlined its importance for
probabilistic logic programming (PLP), lifted inference has been applied up to
now only to relational languages outside of logic programming. In this paper we
adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the
problem of computing the probability of queries to probabilistic logic programs
under the distribution semantics. In particular, we extend the Prolog Factor
Language (PFL) to include two new types of factors that are needed for
representing ProbLog programs. These factors take into account the existing
causal independence relationships among random variables and are managed by the
extension to variable elimination proposed by Zhang and Poole for dealing with
convergent variables and heterogeneous factors. Two new operators are added to
GC-FOVE for treating heterogeneous factors. The resulting algorithm, called
LP for Lifted Probabilistic Logic Programming, has been implemented by
modifying the PFL implementation of GC-FOVE and tested on three benchmarks for
lifted inference. A comparison with PITA and ProbLog2 shows the potential of
the approach.Comment: To appear in Theory and Practice of Logic Programming (TPLP). arXiv
admin note: text overlap with arXiv:1402.0565 by other author
Probability Logic for Harsanyi Type Spaces
Probability logic has contributed to significant developments in belief types
for game-theoretical economics. We present a new probability logic for Harsanyi
Type spaces, show its completeness, and prove both a de-nesting property and a
unique extension theorem. We then prove that multi-agent interactive
epistemology has greater complexity than its single-agent counterpart by
showing that if the probability indices of the belief language are restricted
to a finite set of rationals and there are finitely many propositional letters,
then the canonical space for probabilistic beliefs with one agent is finite
while the canonical one with at least two agents has the cardinality of the
continuum. Finally, we generalize the three notions of definability in
multimodal logics to logics of probabilistic belief and knowledge, namely
implicit definability, reducibility, and explicit definability. We find that
S5-knowledge can be implicitly defined by probabilistic belief but not reduced
to it and hence is not explicitly definable by probabilistic belief
A Probabilistic Logic Programming Event Calculus
We present a system for recognising human activity given a symbolic
representation of video content. The input of our system is a set of
time-stamped short-term activities (STA) detected on video frames. The output
is a set of recognised long-term activities (LTA), which are pre-defined
temporal combinations of STA. The constraints on the STA that, if satisfied,
lead to the recognition of a LTA, have been expressed using a dialect of the
Event Calculus. In order to handle the uncertainty that naturally occurs in
human activity recognition, we adapted this dialect to a state-of-the-art
probabilistic logic programming framework. We present a detailed evaluation and
comparison of the crisp and probabilistic approaches through experimentation on
a benchmark dataset of human surveillance videos.Comment: Accepted for publication in the Theory and Practice of Logic
Programming (TPLP) journa
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