8,095 research outputs found
Inference in Probabilistic Logic Programs using Weighted CNF's
Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. Several classical probabilistic inference
tasks (such as MAP and computing marginals) have not yet received a lot of
attention for this formalism. The contribution of this paper is that we develop
efficient inference algorithms for these tasks. This is based on a conversion
of the probabilistic logic program and the query and evidence to a weighted CNF
formula. This allows us to reduce the inference tasks to well-studied tasks
such as weighted model counting. To solve such tasks, we employ
state-of-the-art methods. We consider multiple methods for the conversion of
the programs as well as for inference on the weighted CNF. The resulting
approach is evaluated experimentally and shown to improve upon the
state-of-the-art in probabilistic logic programming
Expressive Stream Reasoning with Laser
An increasing number of use cases require a timely extraction of non-trivial
knowledge from semantically annotated data streams, especially on the Web and
for the Internet of Things (IoT). Often, this extraction requires expressive
reasoning, which is challenging to compute on large streams. We propose Laser,
a new reasoner that supports a pragmatic, non-trivial fragment of the logic
LARS which extends Answer Set Programming (ASP) for streams. At its core, Laser
implements a novel evaluation procedure which annotates formulae to avoid the
re-computation of duplicates at multiple time points. This procedure, combined
with a judicious implementation of the LARS operators, is responsible for
significantly better runtimes than the ones of other state-of-the-art systems
like C-SPARQL and CQELS, or an implementation of LARS which runs on the ASP
solver Clingo. This enables the application of expressive logic-based reasoning
to large streams and opens the door to a wider range of stream reasoning use
cases.Comment: 19 pages, 5 figures. Extended version of accepted paper at ISWC 201
Lazy Model Expansion: Interleaving Grounding with Search
Finding satisfying assignments for the variables involved in a set of
constraints can be cast as a (bounded) model generation problem: search for
(bounded) models of a theory in some logic. The state-of-the-art approach for
bounded model generation for rich knowledge representation languages, like ASP,
FO(.) and Zinc, is ground-and-solve: reduce the theory to a ground or
propositional one and apply a search algorithm to the resulting theory.
An important bottleneck is the blowup of the size of the theory caused by the
reduction phase. Lazily grounding the theory during search is a way to overcome
this bottleneck. We present a theoretical framework and an implementation in
the context of the FO(.) knowledge representation language. Instead of
grounding all parts of a theory, justifications are derived for some parts of
it. Given a partial assignment for the grounded part of the theory and valid
justifications for the formulas of the non-grounded part, the justifications
provide a recipe to construct a complete assignment that satisfies the
non-grounded part. When a justification for a particular formula becomes
invalid during search, a new one is derived; if that fails, the formula is
split in a part to be grounded and a part that can be justified.
The theoretical framework captures existing approaches for tackling the
grounding bottleneck such as lazy clause generation and grounding-on-the-fly,
and presents a generalization of the 2-watched literal scheme. We present an
algorithm for lazy model expansion and integrate it in a model generator for
FO(ID), a language extending first-order logic with inductive definitions. The
algorithm is implemented as part of the state-of-the-art FO(ID) Knowledge-Base
System IDP. Experimental results illustrate the power and generality of the
approach
Automatic Music Composition using Answer Set Programming
Music composition used to be a pen and paper activity. These these days music
is often composed with the aid of computer software, even to the point where
the computer compose parts of the score autonomously. The composition of most
styles of music is governed by rules. We show that by approaching the
automation, analysis and verification of composition as a knowledge
representation task and formalising these rules in a suitable logical language,
powerful and expressive intelligent composition tools can be easily built. This
application paper describes the use of answer set programming to construct an
automated system, named ANTON, that can compose melodic, harmonic and rhythmic
music, diagnose errors in human compositions and serve as a computer-aided
composition tool. The combination of harmonic, rhythmic and melodic composition
in a single framework makes ANTON unique in the growing area of algorithmic
composition. With near real-time composition, ANTON reaches the point where it
can not only be used as a component in an interactive composition tool but also
has the potential for live performances and concerts or automatically generated
background music in a variety of applications. With the use of a fully
declarative language and an "off-the-shelf" reasoning engine, ANTON provides
the human composer a tool which is significantly simpler, more compact and more
versatile than other existing systems. This paper has been accepted for
publication in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 10 figures. Extended version of our ICLP2008 paper.
Formatted following TPLP guideline
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