292 research outputs found
Introduction to the 26th International Conference on Logic Programming Special Issue
This is the preface to the 26th International Conference on Logic Programming
Special IssueComment: 6 page
Nesting Probabilistic Inference
When doing inference in ProbLog, a probabilistic extension of Prolog, we
extend SLD resolution with some additional bookkeeping. This additional
information is used to compute the probabilistic results for a probabilistic
query. In Prolog's SLD, goals are nested very naturally. In ProbLog's SLD,
nesting probabilistic queries interferes with the probabilistic bookkeeping. In
order to support nested probabilistic inference we propose the notion of a
parametrised ProbLog engine. Nesting becomes possible by suspending and
resuming instances of ProbLog engines. With our approach we realise several
extensions of ProbLog such as meta-calls, negation, and answers of
probabilistic goals.Comment: Online Proceedings of the 11th International Colloquium on
Implementation of Constraint LOgic Programming Systems (CICLOPS 2011),
Lexington, KY, U.S.A., July 10, 201
Machine ethics via logic programming
Machine ethics is an interdisciplinary field of inquiry that emerges from the need of
imbuing autonomous agents with the capacity of moral decision-making. While some
approaches provide implementations in Logic Programming (LP) systems, they have not
exploited LP-based reasoning features that appear essential for moral reasoning.
This PhD thesis aims at investigating further the appropriateness of LP, notably a
combination of LP-based reasoning features, including techniques available in LP systems,
to machine ethics. Moral facets, as studied in moral philosophy and psychology, that
are amenable to computational modeling are identified, and mapped to appropriate LP
concepts for representing and reasoning about them.
The main contributions of the thesis are twofold.
First, novel approaches are proposed for employing tabling in contextual abduction
and updating – individually and combined – plus a LP approach of counterfactual reasoning; the latter being implemented on top of the aforementioned combined abduction and updating technique with tabling. They are all important to model various issues of the aforementioned moral facets.
Second, a variety of LP-based reasoning features are applied to model the identified
moral facets, through moral examples taken off-the-shelf from the morality literature.
These applications include: (1) Modeling moral permissibility according to the Doctrines of Double Effect (DDE) and Triple Effect (DTE), demonstrating deontological and utilitarian judgments via integrity constraints (in abduction) and preferences over abductive scenarios; (2) Modeling moral reasoning under uncertainty of actions, via abduction and probabilistic LP; (3) Modeling moral updating (that allows other – possibly overriding – moral rules to be adopted by an agent, on top of those it currently follows) via the integration of tabling in contextual abduction and updating; and (4) Modeling moral permissibility and its justification via counterfactuals, where counterfactuals are used for formulating DDE.Fundação para a Ciência e a Tecnologia (FCT)-grant SFRH/BD/72795/2010 ; CENTRIA
and DI/FCT/UNL for the supplementary fundin
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
The Magic of Logical Inference in Probabilistic Programming
Today, many different probabilistic programming languages exist and even more
inference mechanisms for these languages. Still, most logic programming based
languages use backward reasoning based on SLD resolution for inference. While
these methods are typically computationally efficient, they often can neither
handle infinite and/or continuous distributions, nor evidence. To overcome
these limitations, we introduce distributional clauses, a variation and
extension of Sato's distribution semantics. We also contribute a novel
approximate inference method that integrates forward reasoning with importance
sampling, a well-known technique for probabilistic inference. To achieve
efficiency, we integrate two logic programming techniques to direct forward
sampling. Magic sets are used to focus on relevant parts of the program, while
the integration of backward reasoning allows one to identify and avoid regions
of the sample space that are inconsistent with the evidence.Comment: 17 pages, 2 figures, International Conference on Logic Programming
(ICLP 2011
A general approach to reasoning with probabilities
We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of knowledge representation formalisms and we study its properties. Syntactically, we consider adding probabilities to the formulas of a given base logic. Semantically, we define a probability distribution over the subsets of a knowledge base by taking the probabilities of the formulas into account accordingly. This gives rise to a probabilistic entailment relation that can be used for uncertain reasoning. Our approach is a generalisation of many concrete probabilistic enrichments of existing approaches, such as ProbLog (an approach to probabilistic logic programming) and the constellation approach to abstract argumentation. We analyse general properties of our approach and provide some insights into novel instantiations that have not been investigated yet
- …