12,481 research outputs found
The PITA System: Tabling and Answer Subsumption for Reasoning under Uncertainty
Many real world domains require the representation of a measure of
uncertainty. The most common such representation is probability, and the
combination of probability with logic programs has given rise to the field of
Probabilistic Logic Programming (PLP), leading to languages such as the
Independent Choice Logic, Logic Programs with Annotated Disjunctions (LPADs),
Problog, PRISM and others. These languages share a similar distribution
semantics, and methods have been devised to translate programs between these
languages. The complexity of computing the probability of queries to these
general PLP programs is very high due to the need to combine the probabilities
of explanations that may not be exclusive. As one alternative, the PRISM system
reduces the complexity of query answering by restricting the form of programs
it can evaluate. As an entirely different alternative, Possibilistic Logic
Programs adopt a simpler metric of uncertainty than probability. Each of these
approaches -- general PLP, restricted PLP, and Possibilistic Logic Programming
-- can be useful in different domains depending on the form of uncertainty to
be represented, on the form of programs needed to model problems, and on the
scale of the problems to be solved. In this paper, we show how the PITA system,
which originally supported the general PLP language of LPADs, can also
efficiently support restricted PLP and Possibilistic Logic Programs. PITA
relies on tabling with answer subsumption and consists of a transformation
along with an API for library functions that interface with answer subsumption
Improving Candidate Quality of Probabilistic Logic Models
Many real-world phenomena exhibit both relational structure and uncertainty. Probabilistic Inductive Logic Programming (PILP) uses Inductive Logic Programming (ILP) extended with probabilistic facts to produce meaningful and interpretable models for real-world phenomena. This merge between First Order Logic (FOL) theories and uncertainty makes PILP a very adequate tool for knowledge representation and extraction. However, this flexibility is coupled with a problem (inherited from ILP) of exponential search space growth and so, often, only a subset of all possible models is explored due to limited resources. Furthermore, the probabilistic evaluation of FOL theories, coming from the underlying probabilistic logic language and its solver, is also computationally demanding. This work introduces a prediction-based pruning strategy, which can reduce the search space based on the probabilistic evaluation of models, and a safe pruning criterion, which guarantees that the optimal model is not pruned away, as well as two alternative more aggressive criteria that do not provide this guarantee. Experiments performed using three benchmarks from different areas show that prediction pruning is effective in (i) maintaining predictive accuracy for all criteria and experimental settings; (ii) reducing the execution time when using some of the more aggressive criteria, compared to using no pruning; and (iii) selecting better candidate models in limited resource settings, also when compared to using no pruning
Towards the Formal Reliability Analysis of Oil and Gas Pipelines
It is customary to assess the reliability of underground oil and gas
pipelines in the presence of excessive loading and corrosion effects to ensure
a leak-free transport of hazardous materials. The main idea behind this
reliability analysis is to model the given pipeline system as a Reliability
Block Diagram (RBD) of segments such that the reliability of an individual
pipeline segment can be represented by a random variable. Traditionally,
computer simulation is used to perform this reliability analysis but it
provides approximate results and requires an enormous amount of CPU time for
attaining reasonable estimates. Due to its approximate nature, simulation is
not very suitable for analyzing safety-critical systems like oil and gas
pipelines, where even minor analysis flaws may result in catastrophic
consequences. As an accurate alternative, we propose to use a
higher-order-logic theorem prover (HOL) for the reliability analysis of
pipelines. As a first step towards this idea, this paper provides a
higher-order-logic formalization of reliability and the series RBD using the
HOL theorem prover. For illustration, we present the formal analysis of a
simple pipeline that can be modeled as a series RBD of segments with
exponentially distributed failure times.Comment: 15 page
Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking
We investigate the suitability of statistical model checking techniques for
analysing quantitative properties of software product line models with
probabilistic aspects. For this purpose, we enrich the feature-oriented
language FLan with action rates, which specify the likelihood of exhibiting
particular behaviour or of installing features at a specific moment or in a
specific order. The enriched language (called PFLan) allows us to specify
models of software product lines with probabilistic configurations and
behaviour, e.g. by considering a PFLan semantics based on discrete-time Markov
chains. The Maude implementation of PFLan is combined with the distributed
statistical model checker MultiVeStA to perform quantitative analyses of a
simple product line case study. The presented analyses include the likelihood
of certain behaviour of interest (e.g. product malfunctioning) and the expected
average cost of products.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301
Relational Neural Machines
Deep learning has been shown to achieve impressive results in several tasks
where a large amount of training data is available. However, deep learning
solely focuses on the accuracy of the predictions, neglecting the reasoning
process leading to a decision, which is a major issue in life-critical
applications. Probabilistic logic reasoning allows to exploit both statistical
regularities and specific domain expertise to perform reasoning under
uncertainty, but its scalability and brittle integration with the layers
processing the sensory data have greatly limited its applications. For these
reasons, combining deep architectures and probabilistic logic reasoning is a
fundamental goal towards the development of intelligent agents operating in
complex environments. This paper presents Relational Neural Machines, a novel
framework allowing to jointly train the parameters of the learners and of a
First--Order Logic based reasoner. A Relational Neural Machine is able to
recover both classical learning from supervised data in case of pure
sub-symbolic learning, and Markov Logic Networks in case of pure symbolic
reasoning, while allowing to jointly train and perform inference in hybrid
learning tasks. Proper algorithmic solutions are devised to make learning and
inference tractable in large-scale problems. The experiments show promising
results in different relational tasks
Quantum Mechanics Unscrambled
Is quantum mechanics about 'states'? Or is it basically another kind of
probability theory? It is argued that the elementary formalism of quantum
mechanics operates as a well-justified alternative to 'classical'
instantiations of a probability calculus. Its providing a general framework for
prediction accounts for its distinctive traits, which one should be careful not
to mistake for reflections of any strange ontology. The suggestion is also made
that quantum theory unwittingly emerged, in Schroedinger's formulation, as a
'lossy' by-product of a quantum-mechanical variant of the Hamilton-Jacobi
equation. As it turns out, the effectiveness of quantum theory qua predictive
algorithm makes up for the computational impracticability of that master
equation.Comment: 25 pages, no figures, final versio
- …